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const JOURNAL_1=[{id:0,page:\"Journal 01\",slug:\"ko-r1-3-data-mix\",kind:\"research\",topic:\"Models\",title:\"Ko-R1-3 Build Log #2, 50 Runs to Find the Best Post Training Mix\",originalTitle:\"Ko-R1-3 Build Log #2, 50 Runs to Find the Best Post Training Mix\",summary:\"Training experiments, data composition and the decisions behind Korean post-training.\",language:\"en\",date:\"Jul 24, 2025\",dateISO:\"2025-07-24\",source:\"https://onelineai.com/en/blog-research/en-record-ko-r1-3-2\",path:\"/research/ko-r1-3-data-mix\",nodes:[{t:\"h2\",a:{id:\"section-1\"},c:[{t:\"strong\",a:{},c:[\"Development of Ko-R1-3 #2\"]}]},{t:\"p\",a:{},c:[\"Through our '\",{t:\"a\",a:{href:\"https://onelineai.com/blog/road-to-ko-r1-3\"},c:[\"Development record of Korean roeasoning model, Ko-R1-3 #1 (2025.07.11)\"]},\"', we examined various aspects of model training. In the meantime, our team, in collaboration with KISTI and ORACLE, carried out approximately 50 training runs across two H200 and two H100 nodes. Within just one week, we conducted large-scale experiments consuming more than 2,000 H200-hours of compute resources.\"]},{t:\"p\",a:{},c:[\"At present, we have collected over 10 million samples for Korean post-training. However, training on the full dataset for five epochs would far exceed the thousands of compute hours we have already used and would surpass our resource budget. As a result, we were compelled to identify the optimal composition of post-training data — determining which samples to retain or discard and which data types to emphasize to maximize model performance. In the following sections, we share the key insights gained from this process.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-2\"},c:[{t:\"strong\",a:{},c:[\"Dataset Composition\"]}]},{t:\"p\",a:{},c:[\"The dataset used for experiments and model training consists of seven distinct categories:\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"OpenThought3\"]},\": We translated the OpenThought3 prompts into Korean using the gemini-2.5-flash-lite-preview-06-17 model. During this process, samples with significant changes in length were filtered out to maintain alignment quality.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"rStar-Coder\"]},\": The same translation and filtering procedure applied to OpenThought3 was also used to construct the rStar-Coder dataset.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Web-Daily/Code/Science/Medical\"]},\": This dataset comprises Korean instruction samples collected from the web, tagged by content type. We removed samples containing images, those that were unreasonably short or long, and samples with unclear intent. While the Web-Daily subset includes some noisy or potentially unsafe prompts, the Code, Science, and Medical subsets are comparatively cleaner and often include domain-specific terminology.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"MCQA-Augmented\"]},': Starting from the KMMLU-Train subset, we incorporated data augmented with varied answer styles, such as responses formatted as \"Answer: N\" or \"\\\\boxed{N}\". Additional augmentation was conducted using BM25 retrieval to gather semantically similar questions, resulting in a more diverse set of multiple-choice QA formats.']}]}]},{t:\"p\",a:{},c:[\"All dataset construction and augmentation procedures were performed using Qwen3-32B. The overall pipeline required approximately 4,000 H100 hours. (At the time of writing, data generation is still ongoing.)\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-3\"},c:[{t:\"strong\",a:{},c:[\"Evaluation Setup\"]}]},{t:\"p\",a:{},c:[\"To mitigate benchmark overfitting of the model, we evaluated existing Korean benchmarks by partitioning them into distinct validation and test sets as follows.\"]},{t:\"figure\",a:{},c:[{t:\"table\",a:{},c:[{t:\"tbody\",a:{},c:[{t:\"tr\",a:{},c:[{t:\"th\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Category\"]}]}]},{t:\"th\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Validation\"]}]}]},{t:\"th\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Test\"]}]}]}]},{t:\"tr\",a:{},c:[{t:\"td\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"General\"]}]}]},{t:\"td\",a:{},c:[{t:\"p\",a:{},c:[\"KMMLU-Redux\"]}]},{t:\"td\",a:{},c:[{t:\"p\",a:{},c:[\"KMMLU-Pro, GPQA\"]}]}]},{t:\"tr\",a:{},c:[{t:\"td\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Reasoning\"]}]}]},{t:\"td\",a:{},c:[{t:\"p\",a:{},c:[\"MCLM-Ko\"]}]},{t:\"td\",a:{},c:[{t:\"p\",a:{},c:[\"HRM8K, LiveCodeBench\"]}]}]},{t:\"tr\",a:{},c:[{t:\"td\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Korean Knowledge\"]}]}]},{t:\"td\",a:{},c:[{t:\"p\",a:{},c:[\"HAE-RAE Bench\"]}]},{t:\"td\",a:{},c:[{t:\"p\",a:{},c:[\"CLicK, KoBALT\"]}]}]},{t:\"tr\",a:{},c:[{t:\"td\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Medical\"]}]}]},{t:\"td\",a:{},c:[{t:\"p\",a:{},c:[\"ClinicalQA\"]}]},{t:\"td\",a:{},c:[{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]}]}]}]}]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"Inference Hyperparameters: \",{t:\"code\",a:{},c:[\"temperature=0.7\"]},\", \",{t:\"code\",a:{},c:[\"top_p=0.9\"]},\", \",{t:\"code\",a:{},c:[\"max_tokens=32768\"]}]}]}]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-4\"},c:[{t:\"strong\",a:{},c:[\"Training Results\"]}]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"Ablation #1: Performance per category\"]}]},{t:\"p\",a:{},c:[\"As a first step, we analyzed the impact of each dataset—OpenThought3, the four web-derived datasets, and MCQA-Augmented—on model performance. To mitigate potential model-specific biases, we conducted experiments using two distinct models: \",{t:\"a\",a:{href:\"https://huggingface.co/kakaocorp/kanana-1.5-8b-instruct-2505\"},c:[\"Kanana-1.5-8B-Instruct\"]},\" and \",{t:\"a\",a:{href:\"https://huggingface.co/google/gemma-3-4b-it\"},c:[\"Gemma3-4B-Instruct\"]},\". Additionally, to ensure that the models could generalize effectively to the MCQA format, a minimal amount of MCQA data was incorporated into the training corpus.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/IHJxIQ7vY7yBzRn4wXZrhnymKfM.png?width=3895&height=1966\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"The results demonstrate that the OpenThought3 data leads to performance improvements across all benchmarks, including the knowledge-intensive HAE-RAE Bench. This indicates that the base model does not fully exploit its own parametric knowledge. Following this, notable gains are observed in the order of MCQA-Augmented, Web-Science, and Web-Code. Contrary to expectations, the inclusion of Web-Medical data had minimal impact on the ClinicalQA score. As a follow-up, we increased the size of the Web-Medical dataset to 100k examples to investigate whether scaling can unlock its hidden value.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/9EWALvmiFpRqMk4IJYSh7x3lK0M.png?width=4400&height=2475\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"When scaling was applied to the Web-Medical dataset, it was observed that the performance either showed no significant improvement or, in some cases, even deteriorated across both models. Consequently, the dataset was excluded entirely from the final training phase.\"]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"Ablation #2: Cross-category benefits\"]}]},{t:\"p\",a:{},c:[\"The data not only contributes to performance improvements when used individually but also yields additional gains when combined with other datasets. Considering this aspect, we further analyzed the effects of data mixing on the collected datasets.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/70l0B5oJWdcDGl4FChzWGzEtA.png?width=4400&height=2475\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"The comparison between Kanana models Ko-R1-3.0.5 and 3.0.6 further underscores the limited utility of the previously used Web-Medical data. In this training setup, replacing Web-Medical with Web-Code led to more substantial performance gains on both the HAE-RAE Bench and MCLM benchmarks. Moreover, we observed a synergistic effect between the Web-Code data and the MCLM benchmark, which motivated the inclusion of translated \",{t:\"a\",a:{href:\"https://huggingface.co/datasets/microsoft/rStar-Coder\"},c:[\"rStar-Coder\"]},\" data—a high-quality code dataset—into the training corpus. (However, the Gemma model did not benefit significantly from the code data.)\"]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"Ablation #3: To Augment or Not To Augment\"]}]},{t:\"p\",a:{},c:[\"Next, we investigated the extent to which the KMMLU-Train set can be augmented. In this process, we employed BM25 to retrieve semantically similar questions and then modified their answer choices to increase the number of options, thereby enhancing the diversity of the dataset.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/xGv2rWLo3SArTC4u0Wq0USJUQyM.png?width=4400&height=2475\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"However, training the model on the augmented data led to degraded performance across most MCQA benchmarks. We hypothesize two primary reasons for this outcome:\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"The BM25-based augmentation of correct answer choices introduced noise that hindered effective learning.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"Since most evaluation benchmarks consist of four options, augmenting the correct choices may have disrupted the intended task structure.\"]}]}]},{t:\"p\",a:{},c:[\"Therefore, in the final training phase, only a small portion of the augmented MCQA dataset was included to preserve diversity without compromising overall performance.\"]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"Ablation #4: Korean-English mix\"]}]},{t:\"p\",a:{},c:[\"For the final model training, we included English data to increase the overall data volume. To leverage English data without compromising performance on Korean, we conducted experiments with the following data ratio settings: 1:1 and 1:4 (Korean:English).\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/SBGqAxqQuSWQk8YTnL02FMgJPI.png?width=4400&height=2475\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"For the Kanana model, consistent with previous experiments, incorporating additional English data leads to decreased performance on KMMLU-Redux and HAE-RAE Bench, while the inclusion of rStar-Coder data improves mathematical reasoning capabilities. In contrast, the Gemma model demonstrates resilience to English data augmentation, exhibiting minimal performance degradation. This discrepancy suggests that Kanana is more sensitive to large-scale Korean reasoning data, whereas Gemma is capable of integrating English data without incurring significant penalties.\"]},{t:\"h2\",a:{id:\"section-5\"},c:[{t:\"strong\",a:{},c:[\"Extra Analysis\"]}]},{t:\"h4\",a:{},c:[\"1. Loss Spikes & Grad-norm\"]},{t:\"p\",a:{},c:[\"Despite being trained on the same data, Gemma exhibited sharp fluctuations in grad-norm and occasional loss spikes. However, these phenomena did not have a significant impact on the final checkpoint.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/qCZd0TzfdSo5qhaz2jnaUoyDm4.png?width=4106&height=921\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"h4\",a:{},c:[\"2. Loss Curves\"]},{t:\"p\",a:{},c:[\"In a single-GPU setting without the use of gradient checkpointing or FSDP, the loss curve exhibited relatively smaller fluctuations. In contrast, in a multi-GPU environment, the loss showed a more step-like drop at each epoch. However, this phenomenon did not have a significant impact on the final performance of the model.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/Qzhx0998LE5q3LvxEl9FZMJxC5Y.png?width=4400&height=1485\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"References\"]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"📝 Original Post - \",{t:\"a\",a:{href:\"https://wandb.ai/guijinson/KoR1-3/reports/What-will-you-do-with-10-000-H100-Hours---VmlldzoxMzY5NTgzNg\"},c:[\"Link\"]}]}]}]}],toc:[{id:\"section-1\",text:\"Development of Ko-R1-3 #2\"},{id:\"section-2\",text:\"Dataset Composition\"},{id:\"section-3\",text:\"Evaluation Setup\"},{id:\"section-4\",text:\"Training Results\"},{id:\"section-5\",text:\"Extra Analysis\"}],images:[\"https://framerusercontent.com/images/IHJxIQ7vY7yBzRn4wXZrhnymKfM.png?width=3895&height=1966\",\"https://framerusercontent.com/images/9EWALvmiFpRqMk4IJYSh7x3lK0M.png?width=4400&height=2475\",\"https://framerusercontent.com/images/70l0B5oJWdcDGl4FChzWGzEtA.png?width=4400&height=2475\",\"https://framerusercontent.com/images/xGv2rWLo3SArTC4u0Wq0USJUQyM.png?width=4400&height=2475\",\"https://framerusercontent.com/images/SBGqAxqQuSWQk8YTnL02FMgJPI.png?width=4400&height=2475\",\"https://framerusercontent.com/images/qCZd0TzfdSo5qhaz2jnaUoyDm4.png?width=4106&height=921\",\"https://framerusercontent.com/images/Qzhx0998LE5q3LvxEl9FZMJxC5Y.png?width=4400&height=1485\"],readMins:5},{id:1,page:\"Journal 02\",slug:\"ko-r1-3-data-mix-ko\",kind:\"research\",topic:\"Models\",title:\"Ko-R1-3 개발기 2편, 50회 대규모 실험으로 최적 데이터 조합 찾기\",originalTitle:\"Ko-R1-3 개발기 2편, 50회 대규모 실험으로 최적 데이터 조합 찾기\",summary:\"한국어 후속 학습을 위한 데이터 조합과 대규모 실험의 기록\",language:\"ko\",date:\"Jul 24, 2025\",dateISO:\"2025-07-24\",source:\"https://onelineai.com/en/blog-research/ko-record-ko-r1-3-2\",path:\"/research/ko-r1-3-data-mix-ko\",nodes:[{t:\"h2\",a:{id:\"section-1\"},c:[{t:\"strong\",a:{},c:[\"Ko-R1-3 개발의 두 번째 챕터\"]}]},{t:\"p\",a:{},c:[\"지난 \",{t:\"a\",a:{href:\"https://onelineai.com/blog/lazy-logging-ko-r1-3\"},c:[\"Ko-R1-3 첫 번째 개발기\"]},\"를 통해서 모델 학습의 다양한 부분에 대해서 살펴보았습니다. 그 사이 저희 팀은 KISTI, ORACLE과 협력하여 H200 2대, H100 2대 노드에서 약 50개의 학습 작업을 진행하며, 단 일주일 만에 2,000 H200-hour이 넘는 리소스를 사용하며 대규모 실험을 진행했습니다.\"]},{t:\"p\",a:{},c:[\"현재 저희는 한국어 Post-training을 위해 1,000만 개가 넘는 샘플을 수집했지만, 이 데이터 전체를 5 epoch 학습시키는 것은 이미 사용한 수천 시간을 훌쩍 넘는, 예산을 초과하는 일이기에 '최적의 Post-training 데이터 조합'을 찾는 것, 어떤 데이터를 남기고, 어떤 데이터를 버릴지, 그리고 어떤 데이터의 비중을 높여야 최고의 성능을 낼 수 있을지 결정해야 했습니다. 지금부터 저희가 이 과정에서 얻은 교훈들에 대해서 소개하겠습니다.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-2\"},c:[{t:\"strong\",a:{},c:[\"데이터셋 구성\"]}]},{t:\"p\",a:{},c:[\"실험과 모델 학습에 사용된 데이터셋은 다음과 같이 총 7개의 카테고리로 구성되어 있습니다:\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"OpenThought3\"]},\": gemini-2.5-flash-lite-preview-06-17을 활용하여 \",{t:\"a\",a:{href:\"https://huggingface.co/datasets/open-thoughts/OpenThoughts3-1.2M\"},c:[\"OpenThought3\"]},\" 프롬프트를 한국어로 번역하고, 번역 과정에서 길이가 지나치게 바뀌는 샘플은 필터링을 진행하였습니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"rStar-Coder\"]},\": OpenThought3와 똑같은 번역 및 필터링 과정을 \",{t:\"a\",a:{href:\"https://huggingface.co/datasets/microsoft/rStar-Coder\"},c:[\"rStar-Coder\"]},\" 데이터셋에 적용하여 데이터셋을 확보하였습니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Web-Daily/Code/Science/Medical\"]},\": 웹으로부터 수집해서 데이터 유형에 따라 태깅이 되어 있는 한국어 instruction 데이터셋. 이때 이미지가 포함되어 있거나 이유없이 길거나 짧은 데이터 샘플을 제거하였습니다. Web-Daily 데이터는 노이즈가 섞인 데이터 혹은 안전하지 않은 프롬프트가 포함되어 있는 반면, Code, Science, Medical 데이터는 더욱 깔끔하게 정리되어 있거나 domain-specific한 전문 용어로 작성되어 있는 데이터로 구성되어 있습니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"MCQA-Augmented\"]},\": KMMLU-Train 서브셋에서 시작해서 ‘정답: N’ 또는 ‘\\\\boxed{N}’과 같이 다양한 응답 스타일로 증강된 데이터가 포함되어 있습니다. 뿐만 아니라 BM25를 통해 검색된 유사한 질문을 활용하여 더욱 다양한 포맷의 MCQA 데이터로 증강하였습니다.\"]}]}]},{t:\"p\",a:{},c:[\"데이터셋 제작 및 증강 과정에는 모두 \",{t:\"code\",a:{},c:[\"Qwen3-32B\"]},\"가 사용되었고, 전체 프로세스에는 대약 4,000 H100 hours기 소요되었습니다. (심지어 아직도 생성중입니다 🤭)\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-3\"},c:[{t:\"strong\",a:{},c:[\"평가 세팅\"]}]},{t:\"p\",a:{},c:[\"모델의 벤치마크 오버피팅을 방지하기 위해 현존하는 한국어 벤치마크를 다음과 같이 validation set과 test set으로 구분하여 평가하였습니다:\"]},{t:\"figure\",a:{},c:[{t:\"table\",a:{},c:[{t:\"tbody\",a:{},c:[{t:\"tr\",a:{},c:[{t:\"th\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Category\"]}]}]},{t:\"th\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Validation\"]}]}]},{t:\"th\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Test\"]}]}]}]},{t:\"tr\",a:{},c:[{t:\"td\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"General\"]}]}]},{t:\"td\",a:{},c:[{t:\"p\",a:{},c:[\"KMMLU-Redux\"]}]},{t:\"td\",a:{},c:[{t:\"p\",a:{},c:[\"KMMLU-Pro, GPQA\"]}]}]},{t:\"tr\",a:{},c:[{t:\"td\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Reasoning\"]}]}]},{t:\"td\",a:{},c:[{t:\"p\",a:{},c:[\"MCLM-Ko\"]}]},{t:\"td\",a:{},c:[{t:\"p\",a:{},c:[\"HRM8K, LiveCodeBench\"]}]}]},{t:\"tr\",a:{},c:[{t:\"td\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Korean Knowledge\"]}]}]},{t:\"td\",a:{},c:[{t:\"p\",a:{},c:[\"HAE-RAE Bench\"]}]},{t:\"td\",a:{},c:[{t:\"p\",a:{},c:[\"CLicK, KoBALT\"]}]}]},{t:\"tr\",a:{},c:[{t:\"td\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Medical\"]}]}]},{t:\"td\",a:{},c:[{t:\"p\",a:{},c:[\"ClinicalQA\"]}]},{t:\"td\",a:{},c:[{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]}]}]}]}]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"평가 하이퍼파라미터: \",{t:\"code\",a:{},c:[\"temperature=0.7\"]},\", \",{t:\"code\",a:{},c:[\"top_p=0.9\"]},\", \",{t:\"code\",a:{},c:[\"max_tokens=32768\"]}]}]}]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-4\"},c:[{t:\"strong\",a:{},c:[\"학습 결과\"]}]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"Ablation #1: 카테고리 별 성능\"]}]},{t:\"p\",a:{},c:[\"첫 번째로 각 데이터셋(OpenThought3, 4개의 웹 데이터, MCQA-Augmented)이 모델의 성능에 미치는 영향을 분석하였습니다. 이때, 모델의 종류에 따른 편향이 발생하지 않도록 \",{t:\"a\",a:{href:\"https://huggingface.co/kakaocorp/kanana-1.5-8b-instruct-2505\"},c:[\"Kanana-1.5-8B-Instruct\"]},\"와 \",{t:\"a\",a:{href:\"https://huggingface.co/google/gemma-3-4b-it\"},c:[\"Gemma3-4B-Instruct\"]},\", 두 모델을 활용하여 실험을 진행하였습니다. 또한 모델이 MCQA 포맷에서도 잘 작동할 수 있도록 최소한의 MCQA 데이터를 모델 학습 데이터에 포함시켰습니다.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/IHJxIQ7vY7yBzRn4wXZrhnymKfM.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"결과를 보면 OpenThought3 데이터가 지식 기반 벤치마크인 HAE-RAE Bench를 포함해서 모든 벤치마크에서 성능 향상을 보여주며 베이스 모델이 모델 자신의 parametric knowledge를 완전히 활용하지 못한다는 것을 보여줍니다. 그 뒤로는 MCQA-Augmented, Web-Science, Web-Code 순으로 큰 성능 향상을 보여줍니다. 하지만 예상과 다르게 Web-Medical 데이터를 사용한다고 ClinicalQA 점수에는 큰 영향이 없었기에 후속 실험으로는 Web-Medical 데이터셋의 크기를 100k로 늘려서 scaling이 hidden value를 해금할 수 있는지를 확인하였습니다.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/9EWALvmiFpRqMk4IJYSh7x3lK0M.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"Web-Medical 데이터를 scaling 했을 때, 두 모델 모두에서 별 효과가 없거나 오히려 성능이 떨어지는 문제들이 발생하기도 했습니다. 따라서 최종 학습에서는 해당 데이터를 완전히 제거하기로 결정하였습니다.\"]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"Ablation #2: 카테고리 교차 효과\"]}]},{t:\"p\",a:{},c:[\"데이터는 혼자일 때 뿐만 아니라 서로 혼합되어 사용될 때, 추가적인 성능 향상을 가져다 주기도 합니다. 이러한 점을 고려하여 수집한 데이터에 대해서도 데이터 혼합의 효과를 분석해보았습니다.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/70l0B5oJWdcDGl4FChzWGzEtA.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"Kanana 모델의 Ko-R1-3.0.5와 3.0.6 모델을 보면 앞선 Web-Medical 데이터의 불필요성을 다시 한 번 확인시켜줍니다. 해당 학습 세팅에서는 Web-Medical 데이터를 Web-Code로 교체한 결과, HAE-RAE Bench와 MCLM에서 보다 큰 성능 향상을 보여주었기 때문입니다. 또한 Web-Code 데이터는 MCLM 벤치마크와 시너지 효과를 보여주는 것을 확인하고, 좋은 품질의 코드 데이터셋인 \",{t:\"a\",a:{href:\"https://huggingface.co/datasets/microsoft/rStar-Coder\"},c:[\"rStar-Coder\"]},\" 데이터를 번역하여 데이터셋에 포함시키기로 결정했습니다. (하지만 Gemma는 코드 데이터로부터 큰 이점을 얻지는 못 했습니다.)\"]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"Ablation #3: 증강하느냐, 하지 않느냐, 그것이 문제로다\"]}]},{t:\"p\",a:{},c:[\"다음으로 KMMLU-Train 세트를 어디까지 증강시킬 수 있는가를 분석하였습니다. 이 과정에서 BM25를 활용하여 비슷한 질문의 answer choice를 꼬아부텨 option의 개수를 늘려 다양성을 부여하였습니다. \"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/xGv2rWLo3SArTC4u0Wq0USJUQyM.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"하지만 증강 결과 데이터로부터 모델을 학습한 결과 대부분의 MCQA 벤치마크에서 오히려 역효과가 발생하였습니다. 이러한 결과가 나오게 된 이유는 다음의 2가지로 유추해볼 수 있습니다:\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"BM25 기반 정답 선택지 증강이 모델 학습에 노이즈 요소로 작용\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"평가 벤치마크가 주로 4개의 선택지로 이루어져 있기에 정답 선택지를 증강하는 것이 역효과를 가져옴\"]}]}]},{t:\"p\",a:{},c:[\"따라서 최종 학습에는 증강된 MCQA 데이터셋을 소수만 포함하여 다양성 유지 역할로 활용하였습니다.\"]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"Ablation #4: 한국어-영어 믹스\"]}]},{t:\"p\",a:{},c:[\"최종 모델 학습에는 데이터의 수를 늘리기 위해 영어 데이터도 함께 포함하고자 합니다. 이때 한국어 성능 저하 없이 영어 데이터를 활용하기 위해 다음과 같이 데이터 비율을 설정하고 실험을 진행하였습니다: 1:1 / 1:4 (ko:en)\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/SBGqAxqQuSWQk8YTnL02FMgJPI.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"Kanana 모델의 경우, 이전 실험들과 비슷하게 영어 데이터를 추가하면 KMMLU-Redux와 HAE-RAE Bench의 성능은 떨어지고, rStar-Coder 데이터를 추가하니 수학 능력이 향상되었습니다. 반면에 Gemma는 큰 손실없이 영어 데이터 혼합을 받아내는 것을 확인할 수 있습니다. 이러한 차이는 Kanana가 방대한 한국어 추론 데이터에 영향을 받지만, Gemma는 아무런 페널티 없이 영어 데이터를 흡수할 수 있다는 것을 시사합니다.\"]},{t:\"h2\",a:{id:\"section-5\"},c:[{t:\"strong\",a:{},c:[\"추가 분석\"]}]},{t:\"h4\",a:{},c:[\"1. Loss Spikes & Grad-norm\"]},{t:\"p\",a:{},c:[\"동일한 데이터에서 학습했음에도 불구하고, Gemma는 샤프한 grad-norm 변동성을 보여주고, 가끔씩 loss spike가 발생하였습니다. 하지만 이러한 현상이 최종 체크포인트에는 큰 영향을 주지 않았습니다.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/qCZd0TzfdSo5qhaz2jnaUoyDm4.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"h4\",a:{},c:[\"2. Loss Curves\"]},{t:\"p\",a:{},c:[\"gradient checkpointing 혹은 FSDP를 사용하지 않은 single-GPU 환경에서는 loss curve가 좀 더 작은 변동폭을 보여줬습니다. 반면에 multi-GPU 환경에서는 매 에폭마다 계단식에 가까운 loss drop을 보여줬습니다. 하지만 이러한 현상도 모델의 최종 성능에는 큰 영향을 주진 않았습니다.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/Qzhx0998LE5q3LvxEl9FZMJxC5Y.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"References\"]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"📝 Original Post - \",{t:\"a\",a:{href:\"https://wandb.ai/guijinson/KoR1-3/reports/What-will-you-do-with-10-000-H100-Hours---VmlldzoxMzY5NTgzNg\"},c:[\"Link\"]}]}]}]}],toc:[{id:\"section-1\",text:\"Ko-R1-3 개발의 두 번째 챕터\"},{id:\"section-2\",text:\"데이터셋 구성\"},{id:\"section-3\",text:\"평가 세팅\"},{id:\"section-4\",text:\"학습 결과\"},{id:\"section-5\",text:\"추가 분석\"}],images:[\"https://framerusercontent.com/images/IHJxIQ7vY7yBzRn4wXZrhnymKfM.png\",\"https://framerusercontent.com/images/9EWALvmiFpRqMk4IJYSh7x3lK0M.png\",\"https://framerusercontent.com/images/70l0B5oJWdcDGl4FChzWGzEtA.png\",\"https://framerusercontent.com/images/xGv2rWLo3SArTC4u0Wq0USJUQyM.png\",\"https://framerusercontent.com/images/SBGqAxqQuSWQk8YTnL02FMgJPI.png\",\"https://framerusercontent.com/images/qCZd0TzfdSo5qhaz2jnaUoyDm4.png\",\"https://framerusercontent.com/images/Qzhx0998LE5q3LvxEl9FZMJxC5Y.png\"],readMins:7},{id:2,page:\"Journal 03\",slug:\"ko-r1-3-development\",kind:\"research\",topic:\"Models\",title:\"Ko-R1-3 Build Log, 7M Data to a Stable Korean Reasoning Model\",originalTitle:\"Ko-R1-3 Build Log, 7M Data to a Stable Korean Reasoning Model\",summary:\"The datasets, training challenges and evaluation choices behind Ko-R1-3.\",language:\"en\",date:\"Jul 14, 2025\",dateISO:\"2025-07-14\",source:\"https://onelineai.com/en/blog-research/en-record-ko-r1-3-1\",path:\"/research/ko-r1-3-development\",nodes:[{t:\"h2\",a:{id:\"section-1\"},c:[{t:\"strong\",a:{},c:[\"The Beginning of Ko-R1-3 Development\"]}]},{t:\"p\",a:{},c:[\"Two years ago, when Stanford's Alpaca introduced a new paradigm for open-source LLM development, \",{t:\"a\",a:{href:\"https://github.com/HAE-RAE\"},c:[\"HAE-RAE\"]},\" was formed with the mission to set the standard for Korean language models. With the goal of establishing a standard for evaluating Korean models, we have contributed to the Korean NLP research ecosystem over the past two years by releasing various benchmarks, including \",{t:\"a\",a:{href:\"https://arxiv.org/abs/2402.11548\"},c:[\"KMMLU\"]},\", \",{t:\"a\",a:{href:\"https://arxiv.org/abs/2309.02706\"},c:[\"HAE-RAE Bench\"]},\", and \",{t:\"a\",a:{href:\"https://arxiv.org/abs/2501.02448\"},c:[\"HRM8K\"]},\".\"]},{t:\"p\",a:{},c:[\"Early this year, the emergence of \",{t:\"a\",a:{href:\"https://arxiv.org/abs/2501.12948\"},c:[\"DeepSeek-R1\"]},\", with its powerful reasoning performance, inspired us to move beyond building benchmarks and dive into model development ourselves. Our first result, \",{t:\"a\",a:{href:\"https://huggingface.co/OLAIR/ko-r1-1.5b-preview\"},c:[\"Ko-R1\"]},\", showed impressive performance on mathematical reasoning problems. However, it also revealed clear limitations in certain areas, struggling to properly answer simple, everyday questions.\"]},{t:\"p\",a:{},c:[\"Building on this experience, OnelineAI, HAE-RAE, and KISTI have collaborated to overcome the limitations of Ko-R1 and have begun developing Ko-R1-3, a next-generation Korean reasoning model with the following strengths:\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Powerful Performance:\"]},\"\\xa0A model that demonstrates strong performance across a wide range of benchmarks, not just in reasoning ability.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Robustness in Everyday Questions:\"]},\"\\xa0A model with excellent robustness in casual conversation, going beyond just knowledge-based or reasoning questions.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Flexible Reasoning Control:\"]},\"\\xa0A model that allows users to flexibly switch between\\xa0\",{t:\"code\",a:{},c:[\"think\"]},\"\\xa0and\\xa0\",{t:\"code\",a:{},c:[\"not_think\"]},\"\\xa0modes according to their preference.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"High Reliability:\"]},\"\\xa0A model that ensures safety, making it trustworthy for anyone to use.\"]}]}]},{t:\"p\",a:{},c:[\"In this post, we will share the challenges we encountered during the initial development of Ko-R1-3 and the solutions we devised to overcome them.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-2\"},c:[{t:\"strong\",a:{},c:[\"Dataset Collection\"]}]},{t:\"p\",a:{},c:[\"Before training, our primary focus was on building a solid foundation to ensure the model could robustly handle not only reasoning tasks but also everyday questions. To achieve this, we collected approximately 7 million data points from various online communities and web pages over the past two months. This raw data then underwent post-processing, including deduplication and image removal. Subsequently, we used the Qwen3-32B model to generate an additional 2 million reasoning-focused data points to create the final training dataset.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-3\"},c:[{t:\"strong\",a:{},c:[\"Model Training & Trial and Error\"]}]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"Loss Spikes\"]}]},{t:\"p\",a:{},c:[\"We discovered an anomaly immediately after starting to train the \",{t:\"a\",a:{href:\"https://huggingface.co/mistralai/Magistral-Small-2506\"},c:[\"Magistral-24B\"]},' model with 130,000 (130K) samples from the full dataset. As you can see in the graph below, the training loss spiked abnormally, which suggested a problem with our training data. Upon analyzing the data in the corresponding batches (89-91) to identify the cause, we found that the issue stemmed from the inclusion of English-only data, instead of our intended \"English reasoning & Korean response\" format.']},{t:\"img\",a:{src:\"https://framerusercontent.com/images/Avci1TvRMZjLchofMTD4bfY6FA.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"p\",a:{},c:[\"To ensure training stability, we applied a filter to keep only the samples that meet the following criteria:\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"Each sample must contain only one\\xa0\",{t:\"code\",a:{},c:[\"<think> ... </think>\"]},\"\\xa0tag.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"The number of Korean tokens in the response generated after the\\xa0\",{t:\"code\",a:{},c:[\"</think>\"]},\"\\xa0tag.\"]}]}]},{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"pre\",a:{},c:[{t:\"code\",a:{},c:[{t:\"span\",a:{},c:[\"def\"]},\" \",{t:\"span\",a:{},c:[\"_analyze_response\"]},{t:\"span\",a:{},c:[\"(\"]},{t:\"span\",a:{},c:[\"text\"]},\": \",{t:\"span\",a:{},c:[\"str\"]},{t:\"span\",a:{},c:[\")\"]},\":\\n    \",{t:\"span\",a:{},c:['\"\"\"Extract the portion after </think> and measure Hangul share.\"\"\"']},\"\\n    \",{t:\"span\",a:{},c:[\"open_ct\"]},\"  = \",{t:\"span\",a:{},c:[\"text\"]},\".\",{t:\"span\",a:{},c:[\"count\"]},{t:\"span\",a:{},c:[\"(\"]},{t:\"span\",a:{},c:['\"<think>\"']},{t:\"span\",a:{},c:[\")\"]},\"\\n    \",{t:\"span\",a:{},c:[\"close_ct\"]},\" = \",{t:\"span\",a:{},c:[\"text\"]},\".\",{t:\"span\",a:{},c:[\"count\"]},{t:\"span\",a:{},c:[\"(\"]},{t:\"span\",a:{},c:['\"</think>\"']},{t:\"span\",a:{},c:[\")\"]},\"\\n\\n\\n    \",{t:\"span\",a:{},c:[\"# Require exactly one <think> … </think> pair\"]},\"\\n    \",{t:\"span\",a:{},c:[\"if\"]},\" \",{t:\"span\",a:{},c:[\"not\"]},\" \",{t:\"span\",a:{},c:[\"(\"]},{t:\"span\",a:{},c:[\"open_ct\"]},\" == \",{t:\"span\",a:{},c:[\"close_ct\"]},\" == \",{t:\"span\",a:{},c:[\"1\"]},{t:\"span\",a:{},c:[\")\"]},\":\\n        \",{t:\"span\",a:{},c:[\"return\"]},\" \",{t:\"span\",a:{},c:[\"None\"]},{t:\"span\",a:{},c:[\",\"]},\" \",{t:\"span\",a:{},c:[\"False\"]},{t:\"span\",a:{},c:[\",\"]},\" \",{t:\"span\",a:{},c:[\"None\"]},{t:\"span\",a:{},c:[\",\"]},\" \",{t:\"span\",a:{},c:[\"None\"]},\"\\n\\n\\n    \",{t:\"span\",a:{},c:[\"tail\"]},\" = \",{t:\"span\",a:{},c:[\"text\"]},\".\",{t:\"span\",a:{},c:[\"split\"]},{t:\"span\",a:{},c:[\"(\"]},{t:\"span\",a:{},c:['\"</think>\"']},{t:\"span\",a:{},c:[\",\"]},\" \",{t:\"span\",a:{},c:[\"1\"]},{t:\"span\",a:{},c:[\")\"]},{t:\"span\",a:{},c:[\"[\"]},{t:\"span\",a:{},c:[\"1\"]},{t:\"span\",a:{},c:[\"]\"]},\"\\n    \",{t:\"span\",a:{},c:[\"total\"]},\" = \",{t:\"span\",a:{},c:[\"len\"]},{t:\"span\",a:{},c:[\"(\"]},{t:\"span\",a:{},c:[\"tail\"]},{t:\"span\",a:{},c:[\")\"]},\"\\n    \",{t:\"span\",a:{},c:[\"korean\"]},\" = \",{t:\"span\",a:{},c:[\"len\"]},{t:\"span\",a:{},c:[\"(\"]},{t:\"span\",a:{},c:[\"re\"]},\".\",{t:\"span\",a:{},c:[\"findall\"]},{t:\"span\",a:{},c:[\"(\"]},{t:\"span\",a:{},c:['r\"[\\\\uac00-\\\\ud7af]\"']},{t:\"span\",a:{},c:[\",\"]},\" \",{t:\"span\",a:{},c:[\"tail\"]},{t:\"span\",a:{},c:[\")\"]},{t:\"span\",a:{},c:[\")\"]},\"  \",{t:\"span\",a:{},c:[\"# Hangul block U+AC00–U+D7AF\"]},\"\\n    \",{t:\"span\",a:{},c:[\"return\"]},\" \",{t:\"span\",a:{},c:[\"tail\"]},{t:\"span\",a:{},c:[\",\"]},\" \",{t:\"span\",a:{},c:[\"True\"]},{t:\"span\",a:{},c:[\",\"]},\" \",{t:\"span\",a:{},c:[\"total\"]},{t:\"span\",a:{},c:[\",\"]},\" \",{t:\"span\",a:{},c:[\"korean\"]}]}]}]}]}]}]}]}]}]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"Loss Drifts\"]}]},{t:\"p\",a:{},c:[\"Immediately after resolving the loss spike issue, we encountered an unexpected problem where the loss began to increase sharply. We identified the cause as the model being stuck in a local minimum and stabilized the loss curve by adjusting the hyperparameters as follows:\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"Decreased the learning rate\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"Increased the weight decay\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"Reduced the number of warm-up steps\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"Changed the optimizer (from 8-bit to AdamW)\"]}]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/wVxkGdL5qsUWQH19sKOSYGxQfRo.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"p\",a:{},c:[\"After stabilizing the dataset and hyperparameter settings, we switched the training model to \",{t:\"a\",a:{href:\"https://huggingface.co/kakaocorp/kanana-1.5-8b-instruct-2505\"},c:[\"Kanana-8B-Instruct\"]},\". When we set a higher learning rate for the Kanana model, we observed the loss curve trending upwards again, so we proceeded to lower the learning rate once more.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/opYjMZWz6h2LMXprwr6HaX6zYqA.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"Optimization of Dataset Composition\"]}]},{t:\"h6\",a:{},c:[{t:\"strong\",a:{},c:[\"1st Run - Ko-R1-3.0.1\"]}]},{t:\"p\",a:{},c:[\"After an initial training run of 5 epochs on a sample dataset (represented by the purple curve in the graph), we saw performance improvements on the MCQA benchmarks (KMMLU-Redux, HAE-RAE Bench). However, there was no significant improvement on the MCLM-ko benchmarks (AIME2024, MATH). This was a concerning sign, as it indicated that the model's performance had stagnated on complex tasks like math, science, and coding—areas where it needs to demonstrate its core reasoning abilities.\"]},{t:\"h6\",a:{},c:[{t:\"strong\",a:{},c:[\"2nd Run - Ko-R1-3.0.2\"]}]},{t:\"p\",a:{},c:[\"Following the first run, we adjusted the dataset composition for the next training phase. First, we replaced the existing math and science data from the Ko-R1 dataset with the higher-quality OpenThought3 dataset. During this process, we translated the OpenThought3 samples into Korean using the Gemini-2.0-Flash model and generated Korean responses with the Qwen3-32B model. Additionally, to investigate the impact of data formatting on model performance, we excluded the MCQA-style KINZ dataset from the training data.\"]},{t:\"p\",a:{},c:[\"The model's evaluation results aligned perfectly with our expectations. Performance improved on the MCLM-Ko benchmarks, while it tended to decrease on the KMMLU-Redux and HAE-RAE Bench. This experiment clearly confirmed two key findings:\"]},{t:\"ol\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"The OpenThought dataset is crucial for enhancing the model's mathematical reasoning capabilities.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"Training data in an MCQA format has a decisive impact on performance in benchmarks of the same format.\"]}]}]},{t:\"h6\",a:{},c:[{t:\"strong\",a:{},c:[\"3rd Run - Ko-R1-3.0.3\"]}]},{t:\"p\",a:{},c:[\"After the second run, we tested training efficiency by sampling the dataset down to 50k samples (about 30% of its previous size) while maintaining the original category distribution. Interestingly, with just 2.7k MCQA samples, the model was able to restore its performance on the KMMLU-Redux and HAE-RAE Bench benchmarks to a level comparable to the previous run. However, performance improvement on the MCLM-ko benchmark was relatively slow, suggesting that further refinement was needed.\"]},{t:\"h6\",a:{},c:[{t:\"strong\",a:{},c:[\"4th Run - Ko-R1-3.0.4\"]}]},{t:\"p\",a:{},c:[\"In the fourth run, we kept the dataset size at 50k samples but replaced the 'daily question' data with 'OpenThought' data. As a result, we were able to achieve performance nearly on par with the second run across most benchmarks, but at only half the training cost.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/5MXSowz5XMf2KCvPoszeyv1dSkg.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/S0ob0tSLvWCywjSROb3cHfF6Ak.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-4\"},c:[{t:\"strong\",a:{},c:[\"Takeaways & Future Plans\"]}]},{t:\"p\",a:{},c:[\"As we developed Ko-R1-3, three key insights emerged:\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"OpenThought Data Scales Beautifully:\"]},\"\\xa0We saw a clear scaling effect with the OpenThought dataset. Simply put, the larger the dataset grew, the better the model performed, showing a direct, proportional benefit.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"MCQA Data is Crucial for Top Scores:\"]},\"\\xa0To excel on both general reasoning and multiple-choice benchmarks, we confirmed once again that having data in the MCQA format is a must.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"'Daily-Question' Data Had Little Effect (For Now):\"]},\"\\xa0On the other hand, we didn't see any significant score improvements on our current benchmarks from using the 'daily-question' data.\"]}]}]},{t:\"p\",a:{},c:[\"However, it's far too early to write off the 'daily-question' data. The issue might not be the data itself, but our tests; current benchmarks simply weren't built to measure the unique benefits this data offers. Going forward, we plan to design new tests to properly assess this capability and search for other scalable dataset combinations beyond just OpenThought.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"References\"]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"📝 Original Post - \",{t:\"a\",a:{href:\"https://wandb.ai/guijinson/KoR1-3/reports/Lazy-Logging-2025-07-11-Ko-R1-3---VmlldzoxMzU0MDgwMw?accessToken=4pwy06g541r5bleatiyfyzjy8fx73obt7744twbeuvmgjmujaywzqmqu32r8lot9\"},c:[\"Link\"]}]}]}]}],toc:[{id:\"section-1\",text:\"The Beginning of Ko-R1-3 Development\"},{id:\"section-2\",text:\"Dataset Collection\"},{id:\"section-3\",text:\"Model Training & Trial and Error\"},{id:\"section-4\",text:\"Takeaways & Future Plans\"}],images:[\"https://framerusercontent.com/images/Avci1TvRMZjLchofMTD4bfY6FA.png\",\"https://framerusercontent.com/images/wVxkGdL5qsUWQH19sKOSYGxQfRo.png\",\"https://framerusercontent.com/images/opYjMZWz6h2LMXprwr6HaX6zYqA.png\",\"https://framerusercontent.com/images/5MXSowz5XMf2KCvPoszeyv1dSkg.png\",\"https://framerusercontent.com/images/S0ob0tSLvWCywjSROb3cHfF6Ak.png\"],readMins:5},{id:3,page:\"Journal 04\",slug:\"ko-r1-3-development-ko\",kind:\"research\",topic:\"Models\",title:\"한국어 추론 모델 Ko-R1-3 개발기, 700만 데이터로 안정화까지\",originalTitle:\"한국어 추론 모델 Ko-R1-3 개발기, 700만 데이터로 안정화까지\",summary:\"데이터 수집부터 학습 안정화까지, Ko-R1-3 개발 과정\",language:\"ko\",date:\"Jul 14, 2025\",dateISO:\"2025-07-14\",source:\"https://onelineai.com/en/blog-research/ko-record-ko-r1-3-1\",path:\"/research/ko-r1-3-development-ko\",nodes:[{t:\"h2\",a:{id:\"section-1\"},c:[{t:\"strong\",a:{},c:[\"Ko-R1-3 개발의 시작\"]}]},{t:\"p\",a:{},c:[\"2년 전, Stanford의 Alpaca가 오픈소스 LLM 개발 연구의 새로운 패러다임을 제시했을 때, \",{t:\"a\",a:{href:\"https://github.com/HAE-RAE\"},c:[\"HAE-RAE\"]},\"는 한국어 언어 모델의 기준을 세우고자 하는 목적으로 결성했습니다. 한국어 모델 평가의 표준을 정립하겠다는 목표 아래, 지난 2년간 \",{t:\"a\",a:{href:\"https://arxiv.org/abs/2402.11548\"},c:[\"KMMLU\"]},\", \",{t:\"a\",a:{href:\"https://arxiv.org/abs/2309.02706\"},c:[\"HAE-RAE Bench\"]},\", \",{t:\"a\",a:{href:\"https://arxiv.org/abs/2501.02448\"},c:[\"HRM8K\"]},\" 등 다양한 벤치마크를 공개하며 국내 NLP 연구 생태계에 기여해왔습니다.\"]},{t:\"p\",a:{},c:[\"올해 초, 강력한 추론 성능을 보여준 DeepSeek-R1의 등장은 벤치마크 구축을 넘어, 직접 모델 개발에 뛰어들게 만들었습니다. 그 첫 결과물인\\xa0\",{t:\"strong\",a:{},c:[\"Ko-R1\"]},\"은 수학 추론 문제에서 인상적인 성능을 보였지만, 간단한 일상 질문에는 제대로 답하지 못하는 등 특정 영역에서의 명확한 한계를 드러냈습니다.\"]},{t:\"p\",a:{},c:[\"이러한 경험을 바탕으로 OnelineAI, HAE-RAE, KISTI는 함께 협력하여 Ko-R1의 한계를 극복하고 다음과 같은 강점을 갖춘 차세대 한국어 추론 모델,\\xa0\",{t:\"strong\",a:{},c:[\"Ko-R1-3\"]},\"\\xa0개발에 착수했습니다.\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"강력한 성능:\"]},\"\\xa0추론 능력은 물론, 다방면의 벤치마크에서 강력한 성능을 입증하는 모델\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"일상 질문에 대한 강건성:\"]},\"\\xa0지식/추론 질문을 넘어, 일상적인 대화에서도 뛰어난 강건성(Robustness)을 갖춘 모델\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"유연한 추론 제어:\"]},\"\\xa0사용자의 기호에 따라 \",{t:\"code\",a:{},c:[\"think/not_think\"]},\" 모드를 유연하게 제어할 수 있는 모델\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"높은 신뢰도:\"]},\"\\xa0안전성(Safety)을 확보하여 누구나 믿고 사용할 수 있는 모델\"]}]}]},{t:\"p\",a:{},c:[\"이번 포스팅에서는 Ko-R1-3 개발 초기에 마주했던 문제들과 문제 해결을 위해 찾아낸 솔루션을 공유하고자 합니다.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-2\"},c:[{t:\"strong\",a:{},c:[\"데이터셋 수집\"]}]},{t:\"p\",a:{},c:[\"모델 학습에 앞서, 모델이 추론 문제뿐만 아니라 일상적인 질문에도\\xa0\",{t:\"strong\",a:{},c:[\"강건하게(robust)\"]},\"\\xa0대답할 수 있도록 기반을 다지는 데 주력했습니다. 이를 위해 지난 2개월간 다양한 온라인 커뮤니티와 웹페이지에서 약 700만 건의 데이터를 수집했습니다. 수집한 데이터는 중복 제거(deduplication)와 이미지 제거 등의 후처리 과정을 거쳤습니다. 이후 Qwen3-32B 모델을 활용해 약 200만 건의 추론 데이터를\\xa0\",{t:\"strong\",a:{},c:[\"생성(증강)하여\"]},\"\\xa0최종 학습 데이터셋을 준비했습니다. (※ 제거된 이미지 데이터는 추후 공개할 VLM 벤치마크 제작에 활용될 예정입니다.)\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-3\"},c:[{t:\"strong\",a:{},c:[\"모델 학습과 시행착오\"]}]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"Loss Spikes\"]}]},{t:\"p\",a:{},c:[\"전체 데이터셋에서 샘플링한 13만 건(130K)의 데이터로 \",{t:\"a\",a:{href:\"https://huggingface.co/mistralai/Magistral-Small-2506\"},c:[\"Magistral-24B\"]},\" 모델 학습을 시작하자마자 이상 징후를 발견했습니다. 아래 그래프에서 볼 수 있듯,\\xa0\",{t:\"strong\",a:{},c:[\"Loss가 비정상적으로 급등하는 스파이크(spike) 현상\"]},\"이 나타났고, 이는 학습 데이터에 문제가 있음을 시사했습니다. 원인 파악을 위해 해당 구간(89~91번 배치)의 데이터를 분석한 결과, 의도했던 '영어 추론 & 한국어 응답' 데이터가 아닌\\xa0\",{t:\"strong\",a:{},c:[\"영어로만 구성된 데이터가 포함된 것\"]},\"이 원인이었습니다.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/Avci1TvRMZjLchofMTD4bfY6FA.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"p\",a:{},c:[\"학습의 안정성을 위해, 다음 기준을 만족하는 샘플만 필터링하는 코드를 적용했습니다.\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"각 샘플에\\xa0\",{t:\"code\",a:{},c:[\"<think> … </think>\"]},\"\\xa0태그가\\xa0\",{t:\"strong\",a:{},c:[\"하나만 존재\"]},\"할 것\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"code\",a:{},c:[\"</think>\"]},\"\\xa0태그 이후에 생성된 응답 중\\xa0\",{t:\"strong\",a:{},c:[\"한국어 토큰의 수\"]}]}]}]},{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"pre\",a:{},c:[{t:\"code\",a:{},c:[{t:\"span\",a:{},c:[\"def\"]},\" \",{t:\"span\",a:{},c:[\"_analyze_response\"]},{t:\"span\",a:{},c:[\"(\"]},{t:\"span\",a:{},c:[\"text\"]},\": \",{t:\"span\",a:{},c:[\"str\"]},{t:\"span\",a:{},c:[\")\"]},\":\\n    \",{t:\"span\",a:{},c:['\"\"\"Extract the portion after </think> and measure Hangul share.\"\"\"']},\"\\n    \",{t:\"span\",a:{},c:[\"open_ct\"]},\"  = \",{t:\"span\",a:{},c:[\"text\"]},\".\",{t:\"span\",a:{},c:[\"count\"]},{t:\"span\",a:{},c:[\"(\"]},{t:\"span\",a:{},c:['\"<think>\"']},{t:\"span\",a:{},c:[\")\"]},\"\\n    \",{t:\"span\",a:{},c:[\"close_ct\"]},\" = \",{t:\"span\",a:{},c:[\"text\"]},\".\",{t:\"span\",a:{},c:[\"count\"]},{t:\"span\",a:{},c:[\"(\"]},{t:\"span\",a:{},c:['\"</think>\"']},{t:\"span\",a:{},c:[\")\"]},\"\\n\\n\\n    \",{t:\"span\",a:{},c:[\"# Require exactly one <think> … </think> pair\"]},\"\\n    \",{t:\"span\",a:{},c:[\"if\"]},\" \",{t:\"span\",a:{},c:[\"not\"]},\" \",{t:\"span\",a:{},c:[\"(\"]},{t:\"span\",a:{},c:[\"open_ct\"]},\" == \",{t:\"span\",a:{},c:[\"close_ct\"]},\" == \",{t:\"span\",a:{},c:[\"1\"]},{t:\"span\",a:{},c:[\")\"]},\":\\n        \",{t:\"span\",a:{},c:[\"return\"]},\" \",{t:\"span\",a:{},c:[\"None\"]},{t:\"span\",a:{},c:[\",\"]},\" \",{t:\"span\",a:{},c:[\"False\"]},{t:\"span\",a:{},c:[\",\"]},\" \",{t:\"span\",a:{},c:[\"None\"]},{t:\"span\",a:{},c:[\",\"]},\" \",{t:\"span\",a:{},c:[\"None\"]},\"\\n\\n\\n    \",{t:\"span\",a:{},c:[\"tail\"]},\" = \",{t:\"span\",a:{},c:[\"text\"]},\".\",{t:\"span\",a:{},c:[\"split\"]},{t:\"span\",a:{},c:[\"(\"]},{t:\"span\",a:{},c:['\"</think>\"']},{t:\"span\",a:{},c:[\",\"]},\" \",{t:\"span\",a:{},c:[\"1\"]},{t:\"span\",a:{},c:[\")\"]},{t:\"span\",a:{},c:[\"[\"]},{t:\"span\",a:{},c:[\"1\"]},{t:\"span\",a:{},c:[\"]\"]},\"\\n    \",{t:\"span\",a:{},c:[\"total\"]},\" = \",{t:\"span\",a:{},c:[\"len\"]},{t:\"span\",a:{},c:[\"(\"]},{t:\"span\",a:{},c:[\"tail\"]},{t:\"span\",a:{},c:[\")\"]},\"\\n    \",{t:\"span\",a:{},c:[\"korean\"]},\" = \",{t:\"span\",a:{},c:[\"len\"]},{t:\"span\",a:{},c:[\"(\"]},{t:\"span\",a:{},c:[\"re\"]},\".\",{t:\"span\",a:{},c:[\"findall\"]},{t:\"span\",a:{},c:[\"(\"]},{t:\"span\",a:{},c:['r\"[\\\\uac00-\\\\ud7af]\"']},{t:\"span\",a:{},c:[\",\"]},\" \",{t:\"span\",a:{},c:[\"tail\"]},{t:\"span\",a:{},c:[\")\"]},{t:\"span\",a:{},c:[\")\"]},\"  \",{t:\"span\",a:{},c:[\"# Hangul block U+AC00–U+D7AF\"]},\"\\n    \",{t:\"span\",a:{},c:[\"return\"]},\" \",{t:\"span\",a:{},c:[\"tail\"]},{t:\"span\",a:{},c:[\",\"]},\" \",{t:\"span\",a:{},c:[\"True\"]},{t:\"span\",a:{},c:[\",\"]},\" \",{t:\"span\",a:{},c:[\"total\"]},{t:\"span\",a:{},c:[\",\"]},\" \",{t:\"span\",a:{},c:[\"korean\"]}]}]}]}]}]}]}]}]}]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"Loss Drifts\"]}]},{t:\"p\",a:{},c:[\"Loss Spike 문제를 해결한 직후, 오히려 loss가 급격히 증가하는 현상이 발생했습니다. 해당 현상의 원인을 모델이 local minimum에 빠진 것으로 판단하고, 다음과 같이 하이퍼파라미터를 조정하여 loss curve를 안정시켰습니다.\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"Learning Rate 감축\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"Weight Decay 증가\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"Warm-up 스텝 감축\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"Optimizer 변경 (8-bit → AdamW)\"]}]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/wVxkGdL5qsUWQH19sKOSYGxQfRo.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"p\",a:{},c:[\"데이터셋과 하이퍼파라미터\\xa0설정이 안정화된 후, 학습 모델을 \",{t:\"a\",a:{href:\"https://huggingface.co/kakaocorp/kanana-1.5-8b-instruct-2505\"},c:[\"Kanana-8B-Instruct\"]},\"로 교체했습니다. Kanana 모델에 learning rate를 높게\\xa0\",{t:\"strong\",a:{},c:[\"설정하자\"]},\"\\xa0loss curve가 다시 우상향하는\\xa0현상을 확인하고, learning rate를 다시 한번 낮추는 작업을 진행했습니다.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/opYjMZWz6h2LMXprwr6HaX6zYqA.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"최적의 데이터셋 구성\"]}]},{t:\"h6\",a:{},c:[{t:\"strong\",a:{},c:[\"1st run - Ko-R1-3.0.1\"]}]},{t:\"p\",a:{},c:[\"샘플 데이터셋으로 5 epoch의 초기 학습을 진행한 결과(그래프의 보라색 커브), MCQA 벤치마크(KMMLU-Redux, HAE-RAE Bench)에서는 성능이 향상되었지만, \",{t:\"a\",a:{href:\"https://huggingface.co/datasets/amphora/MCLM\"},c:[\"MCLM-ko\"]},\"(AIME2024, MATH)에서는 유의미한 개선이 없었습니다. 이는 모델이\\xa0추론 모델의 핵심 역량을 보여주어야 할\\xa0수학, 과학, 코딩과 같은 고난도 태스크에서 성능이 정체되었다는 의미였기에, 좋지 않은 신호였습니다.\"]},{t:\"h6\",a:{},c:[{t:\"strong\",a:{},c:[\"2nd run - Ko-R1-3.0.2\"]}]},{t:\"p\",a:{},c:[\"첫 번째 학습 이후, 데이터셋 구성을 다음과 같이 변경하여 모델 학습을 진행했습니다. 먼저, 기존 Ko-R1 데이터셋에 포함된 수학 및 과학 데이터를 더 높은 품질의\\xa0OpenThought3 데이터셋으로 교체했습니다. 이 과정에서 \",{t:\"a\",a:{href:\"https://huggingface.co/datasets/open-thoughts/OpenThoughts3-1.2M\"},c:[\"OpenThought3\"]},\" 샘플은 Gemini-2.0-Flash 모델을 이용해 한국어로 번역하고, \",{t:\"a\",a:{href:\"https://huggingface.co/Qwen/Qwen3-32B\"},c:[\"Qwen3-32B\"]},\" 모델로 한국어 응답을 생성했습니다. 또한, 데이터 포맷팅이 모델 성능에 미치는 영향을 확인하고자\\xa0MCQA 유형의 KINZ 데이터셋을 학습에서 제외했습니다.\"]},{t:\"p\",a:{},c:[\"학습 모델 평가 결과는 예상과 정확히 일치했습니다.\\xa0MCLM-Ko에서는 성능이 상승했지만,\\xa0KMMLU-Redux와 HAE-RAE Bench에서는 성능이 하락하는 경향을 보였습니다. 이 실험을 통해 두 가지 중요한 사실을 명확히 확인할 수 있었습니다: 첫째, OpenThought 데이터셋이 모델의 수학적 추론 능력 향상에 핵심적인 역할을 한다는 것. 둘째, MCQA 형식의 학습 데이터가 동일한 형식의 벤치마크 성능에 결정적인 영향을 미친다는 것입니다.\"]},{t:\"h6\",a:{},c:[{t:\"strong\",a:{},c:[\"3rd run - Ko-R1-3.0.3\"]}]},{t:\"p\",a:{},c:[\"두 번째 학습 이후, 기존 데이터셋의 카테고리 분포는 그대로 유지하면서 전체 크기만 약 30%인 50k로 샘플링하여 학습 효율을 점검했습니다. 흥미롭게도 단 2.7k의 MCQA 샘플만으로 KMMLU-Redux와 HAE-RAE Bench 벤치마크에서 기존 모델과 대등한 수준의 성능을 복원할 수 있었습니다. 하지만 MCLM-ko 벤치마크에서는 상대적으로 성능 개선이 더딘 모습을 보여, 추가적인 보완이 필요함을 시사했습니다.\"]},{t:\"h6\",a:{},c:[{t:\"strong\",a:{},c:[\"4th run - Ko-R1-3.0.4\"]}]},{t:\"p\",a:{},c:[\"데이터 샘플 수는 50k로 유지하면서, 'daily question'을 'OpenThought' 데이터로 교체했습니다. 그 결과, 절반의 학습 비용만으로도 대부분의 벤치마크에서 두 번째 학습과 거의 비슷한 수준의 성능을 달성할 수 있었습니다.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/5MXSowz5XMf2KCvPoszeyv1dSkg.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/S0ob0tSLvWCywjSROb3cHfF6Ak.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-4\"},c:[{t:\"strong\",a:{},c:[\"Takeaways & Future Plans\"]}]},{t:\"p\",a:{},c:[\"Ko-R1-3 개발을 진행하며 다음과 같은 세 가지 중요한 사실을 확인할 수 있었습니다.\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"OpenThought 데이터의 확장성:\"]},\"\\xa0OpenThought 데이터셋은 그 규모가 커질수록 모델의 성능 향상에 비례하여 기여하는, 뚜렷한 scaling의 효고라르 보여주었습니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"MCQA 데이터의 중요성:\"]},\"\\xa0추론 벤치마크 뿐만 아니라 MCQA 벤치마크에서도 높은 점수를 달성하기 위해서는 MCQA 형식의 데이터가 필수적이라는 점을 다시 한번 확인했습니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"'일상 질문' 데이터의 미미한 영향:\"]},\"\\xa0반면, 'daily-question' 데이터는 현재 사용 중인 벤치마크 점수에는 눈에 띄는 영향을 주지 않는 것으로 나타났습니다.\"]}]}]},{t:\"p\",a:{},c:[\"물론 'daily-question' 데이터의 효과가 없다고 단정하기는 이릅니다. 이는 현재의 벤치마크가 해당 데이터가 주는 이점을 측정하는데 초점을 두고 만들어지지 않았기 때문입니다. 따라서 이 능력을 정밀하게 평가하기 위한 새로운 테스트를 진행하며, OpenThought 외 다른 데이터셋에서도 유의미한 확장성을 보이는 조합이 있는지 탐색할 계획입니다.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"References\"]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"📝 Original Post - \",{t:\"a\",a:{href:\"https://wandb.ai/guijinson/KoR1-3/reports/Lazy-Logging-2025-07-11-Ko-R1-3---VmlldzoxMzU0MDgwMw?accessToken=4pwy06g541r5bleatiyfyzjy8fx73obt7744twbeuvmgjmujaywzqmqu32r8lot9\"},c:[\"Link\"]}]}]}]}],toc:[{id:\"section-1\",text:\"Ko-R1-3 개발의 시작\"},{id:\"section-2\",text:\"데이터셋 수집\"},{id:\"section-3\",text:\"모델 학습과 시행착오\"},{id:\"section-4\",text:\"Takeaways & Future Plans\"}],images:[\"https://framerusercontent.com/images/Avci1TvRMZjLchofMTD4bfY6FA.png\",\"https://framerusercontent.com/images/wVxkGdL5qsUWQH19sKOSYGxQfRo.png\",\"https://framerusercontent.com/images/opYjMZWz6h2LMXprwr6HaX6zYqA.png\",\"https://framerusercontent.com/images/5MXSowz5XMf2KCvPoszeyv1dSkg.png\",\"https://framerusercontent.com/images/S0ob0tSLvWCywjSROb3cHfF6Ak.png\"],readMins:7},{id:4,page:\"Journal 05\",slug:\"spot-verification\",kind:\"research\",topic:\"Evaluation\",title:\"SPOT Benchmark Reveals Multimodal LLM Verification Limits, Finds Errors Without Confidence\",originalTitle:\"SPOT Benchmark Reveals Multimodal LLM Verification Limits, Finds Errors Without Confidence\",summary:\"What scientific-paper verification reveals about multimodal model evaluation.\",language:\"en\",date:\"May 30, 2025\",dateISO:\"2025-05-30\",source:\"https://onelineai.com/en/blog-research/when-ai-co-scientist-fail-spot-en\",path:\"/research/spot-verification\",nodes:[{t:\"blockquote\",a:{},c:[{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:['\"',{t:\"a\",a:{href:\"https://arxiv.org/abs/2505.11855\"},c:[\"When AI Co-Scientists Fail: SPOT-a Benchmark for Automated Verification of Scientific Research\"]},'\" 논문을 기반으로 다국어 환경에서 LLM의 Test-Time Scaling의 효과에 대해 분석하는 내용을 다루고 있습니다. ']}]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"이 시리즈는 원라인에이아이에서 발표한 논문을 리뷰합니다. 논문과 관련해서 궁금한 내용이 있다면 \",{t:\"a\",a:{href:\"https://www.onelineai.com/contact\"},c:[{t:\"strong\",a:{},c:[\"원라인에이아이 AI팀\"]}]},\"에게 문의주시기 바랍니다.\"]}]}]}]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-1\"},c:[{t:\"strong\",a:{},c:[\"The potential of AI Co-Scientists: What about verification?\"]}]},{t:\"p\",a:{},c:['With the recent advances in large language models (LLMs), automated research is gaining attention across various fields. Amid this trend, the concept of so-called \"AI Co-Scientists\" has emerged, where AI systems partially replace or complement human researchers in the scientific discovery process, from hypothesis generation to writing papers. However, despite their success in generating research outcomes, their capabilities as \"verifiers\" — identifying errors or validating the content of academic papers — have not been thoroughly explored.']},{t:\"p\",a:{},c:[\"Focusing precisely on this gap, the research team at OneLineAI proposed a benchmark dataset called SPOT (Scientific Paper Error Detection) to evaluate the academic verification abilities of LLMs. SPOT is a dataset based on 83 published papers, compiled by extracting actual errors acknowledged by the authors or found in retracted papers.\"]},{t:\"p\",a:{},c:[\"Using this benchmark dataset, the team conducted experiments to assess how well state-of-the-art multi-modal LLMs can detect scientific errors. They also analyzed the current limitations of these models as verifiers. In this post, we take a closer look at what the SPOT benchmark entails, based on the paper\\xa0\",{t:\"em\",a:{},c:['\"When AI Co-Scientists Fail: SPOT — a Benchmark for Automated Verification of Scientific Research,\"']},\"\\xa0and deeply examine how capable the latest models are in serving as scientific verifiers.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-2\"},c:[{t:\"strong\",a:{},c:[\"Watch out! I SPOT the errors!\"]}]},{t:\"p\",a:{},c:[\"SPOT is a multi-modal error detection benchmark built upon 83 recent scientific papers published after 2024. It includes 91 significant errors that authors themselves acknowledged and either corrected or retracted in their actual papers. Each paper in the dataset contains approximately 12,887 tokens and around 18 images, making SPOT far more complex and comprehensive than existing datasets for error validation. The SPOT benchmark was created through the following five-step process:\"]},{t:\"ol\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Initial Data Collection\"]},\": Papers with reported errors were selected from \",{t:\"a\",a:{href:\"https://arxiv.org/abs/2412.03775\"},c:[\"WithdrarXiv\"]},\" and \",{t:\"a\",a:{href:\"https://pubpeer.com/\"},c:[\"PubPeer\"]},\".\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Automated Filtering\"]},\": The GPT-4o model was used to filter for clear and unambiguous errors.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Author Confirmation\"]},\": Only errors acknowledged by the original authors were included in the dataset.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Human Annotation\"]},\": Human annotators reviewed and validated errors for clarity and severity.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Data Normalization\"]},\": Finally, OCR technology was used to extract and standardize text and image data from PDFs, enabling precise verification of the errors.\"]}]}]},{t:\"p\",a:{},c:[\"Through this process, SPOT covers papers from the following ten research domains and addresses six types of errors:\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Research Domains\"]},\": Mathematics, Physics, Biology, Chemistry, Materials Science, Medicine, Environmental Science, Engineering, Computer Science, Multidisciplinary\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Error Types\"]},\": Equation or proof, Figure duplication, Data inconsistency, Statistical reporting, Reagent identity, Experiment setup\"]}]}]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/LFoHG1IrrpihOi7KQpUFJRLBjuI.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"p\",a:{},c:[\"SPOT uses the following three key evaluation metrics to assess the model's performance in detecting and verifying errors in scientific papers:\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Precision\"]},\": The proportion of errors identified by the model that are actually confirmed as real errors.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Recall\"]},\": Measures how many of the real errors identified by humans in the SPOT dataset are correctly detected by the model.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"pass@K\"]},\": An indicator that evaluates whether the model can identify at least one actual error when it attempts error detection K times on a single paper.\"]}]}]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-3\"},c:[{t:\"strong\",a:{},c:[\"Can the current models effectively SPOT errors?\"]}]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/EWOKJSqoMXjVcrkyMqQhO31w7g.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"p\",a:{},c:[\"Through SPOT benchmark evaluations on various proprietary and open-source multi-modal models, the following analyses were identified:\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"High level of difficulty\"]},\": Open-source models showed near-zero performance in multi-modal environments and struggled with error detection. In text-only environments, they performed slightly better than in multi-modal settings. Similarly, most proprietary models also showed near-zero performance and difficulty in detecting errors, with the exception of o3 and Gemini-2.5-Pro.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Utilization of multi-modal environments by models\"]},\": Understanding academic papers requires not only text but also images. However, the results show that most models perform better in text-only environments than in multi-modal ones. This suggests that most models are not effectively utilizing image information for error detection. Only o3 and Gemini-2.5-Pro showed lower performance in text-only environments compared to multi-modal ones, indicating that they are appropriately referencing image information.\"]}]}]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/5u4r0E379Jeuuj0oJjtWTt11xl4.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"p\",a:{},c:[\"Additionally, when examining the confidence and pass@4 performance of each model, we can observe some intriguing results. Looking at the graph on the right in the figure above, we see that o3 gets all the answers correct in the Statistical Reporting error category, and the other models also show fairly high accuracy. However, since the confidence scores of all models do not exceed 0.5, this suggests that rather than detecting the errors with certainty, the models are more likely guessing correctly.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-4\"},c:[{t:\"strong\",a:{},c:[\"Conclusion\"]}]},{t:\"p\",a:{},c:[\"LLMs are now demonstrating strong performance not only in the field of computer science but also across various scientific domains. As a result, their role is becoming increasingly important not only as AI Co-Scientists that formulate hypotheses and conduct experiments but also as agents that verify the integrity of scientific research. In the paper titled\\xa0\",{t:\"em\",a:{},c:['\"When AI Co-Scientists Fail: SPOT—a Benchmark for Automated Verification of Scientific Research,\"']},\"\\xa0published by the OneLine AI research team, the SPOT benchmark was used to analyze the potential of different language models as verifiers.\"]},{t:\"p\",a:{},c:[\"The experimental results show that unless models are high-performing ones like o3 or Gemini 2.5 Pro, it is difficult to properly utilize multi-modal data. Even these advanced models produce verification outcomes with such low confidence that they cannot be considered reliable. These findings suggest that current language models still have many shortcomings in their role as AI Co-Scientists. It is hoped that further active research will be conducted based on the SPOT benchmark and its published experimental results to enable AI Co-Scientists to serve as trustworthy verifiers.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"References\"]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"📝 Paper: \",{t:\"a\",a:{href:\"https://arxiv.org/abs/2505.11855\"},c:[\"https://arxiv.org/abs/2505.11855\"]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"🤗 Dataset: \",{t:\"a\",a:{href:\"https://huggingface.co/datasets/amphora/SPOT-MetaData\"},c:[\"https://huggingface.co/datasets/amphora/SPOT-MetaData\"]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"🖥️ GitHub: \",{t:\"a\",a:{href:\"https://github.com/guijinSON/SPOT\"},c:[\"https://github.com/guijinSON/SPOT\"]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"🖥️ Blog: \",{t:\"a\",a:{href:\"https://llms-in-science.github.io/spot/\"},c:[\"https://llms-in-science.github.io/spot/\"]}]}]}]}],toc:[{id:\"section-1\",text:\"The potential of AI Co-Scientists: What about verification?\"},{id:\"section-2\",text:\"Watch out! I SPOT the errors!\"},{id:\"section-3\",text:\"Can the current models effectively SPOT errors?\"},{id:\"section-4\",text:\"Conclusion\"}],images:[\"https://framerusercontent.com/images/LFoHG1IrrpihOi7KQpUFJRLBjuI.png\",\"https://framerusercontent.com/images/EWOKJSqoMXjVcrkyMqQhO31w7g.png\",\"https://framerusercontent.com/images/5u4r0E379Jeuuj0oJjtWTt11xl4.png\"],readMins:5},{id:5,page:\"Journal 06\",slug:\"spot-verification-ko\",kind:\"research\",topic:\"Evaluation\",title:\"SPOT 벤치마크로 본 멀티모달 LLM 검증의 한계, 오류는 찾지만 확신은 없다\",originalTitle:\"SPOT 벤치마크로 본 멀티모달 LLM 검증의 한계, 오류는 찾지만 확신은 없다\",summary:\"과학 논문의 실제 오류를 통해 살펴본 멀티모달 모델의 검증 능력\",language:\"ko\",date:\"May 30, 2025\",dateISO:\"2025-05-30\",source:\"https://onelineai.com/en/blog-research/when-ai-co-scientist-fail-spot-ko\",path:\"/research/spot-verification-ko\",nodes:[{t:\"blockquote\",a:{},c:[{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:['\"',{t:\"a\",a:{href:\"https://arxiv.org/abs/2505.11855\"},c:[\"When AI Co-Scientists Fail: SPOT-a Benchmark for Automated Verification of Scientific Research\"]},'\" 논문을 기반으로 다국어 환경에서 LLM의 Test-Time Scaling의 효과에 대해 분석하는 내용을 다루고 있습니다. ']}]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"이 시리즈는 원라인에이아이에서 발표한 논문을 리뷰합니다. 논문과 관련해서 궁금한 내용이 있다면 \",{t:\"a\",a:{href:\"https://www.onelineai.com/contact\"},c:[{t:\"strong\",a:{},c:[\"원라인에이아이 AI팀\"]}]},\"에게 문의주시기 바랍니다.\"]}]}]}]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-1\"},c:[{t:\"strong\",a:{},c:[\"AI Co-Scientist의 가능성, 검증에서는 어떨까?\"]}]},{t:\"p\",a:{},c:[\"최근 LLM의 발전으로 인해 다양한 분야에서 자동화된 연구가 주목받고 있습니다. 이러한 흐름 속에서 이른바 'AI 연구자(AI Co-Scientists)'라는 개념이 등장하여 과학적 발견 과정에서 가설 생성부터 논문 작성까지 인간 연구자의 역할을 부분적으로 대체하거나 보완하고 있습니다. 그러나 연구의 결과물을 생성하는 역할에서의 성공에도 불구하고, 학술 논문의 내용을 검증하거나 오류를 찾아내는 '검증자'로서의 능력은 아직까지 충분히 탐구되지 않았습니다.\"]},{t:\"p\",a:{},c:[\"원라인에이아이 연구팀은 바로 이 지점에 초점을 맞추면서 LLM의 학술 검증 능력을 평가하기 위한 벤치마크 데이터셋인 SPOT(\",{t:\"strong\",a:{},c:[\"S\"]},\"cientific \",{t:\"strong\",a:{},c:[\"P\"]},\"aper Err\",{t:\"strong\",a:{},c:[\"o\"]},\"r De\",{t:\"strong\",a:{},c:[\"t\"]},\"ection)를 제안했습니다. SPOT은 실제로 출판된 논문 83편을 바탕으로 구성된 데이터셋으로, 이 논문들에서 저자가 인정한 오류 또는 논문이 철회된 사례와 같은 실질적인 오류를 추출하여 제작되었습니다.\"]},{t:\"p\",a:{},c:['연구팀은 해당 벤치마크 데이터셋을 활용하여 최신 Multi-modal LLM들이 과학적 오류를 얼마나 잘 식별할 수 있는지 실험하고 평가하며, 현재 모델들의 verifier로써의 부족함을 분석하였습니다. 본 포스팅에서는 \"When AI Co-Scientists Fail: SPOT-a Benchmark for Automated Verification of Scientific Research\" 논문을 토대로 SPOT 벤치마크가 무엇인지 살펴보고 최신 모델들이 과학적 검증자로써 어느 정도의 역량을 보여주는지 심층적으로 분석해보겠습니다.']},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-2\"},c:[{t:\"strong\",a:{},c:[\"Watch out! I SPOT the errors!\"]}]},{t:\"p\",a:{},c:[\"SPOT은 2024년 이후 발표된 최신 과학 논문 83편을 기반으로 구축된 multi-modal error detection 벤치마크로, 실제 논문에서 저자들이 인정하고 정정이나 철회를 해야 했던 중대한 오류 91개를 포함하고 있습니다. 또한 논문 한 편 당 약 12,887개의 토큰과 약 18개의 이미지를 포함하고 있어, 기존의 오류 타 오류 검증 데이터셋보다 훨씬 복잡하고 포괄적인 자료를 제공합니다. 이러한 SPOT 벤치마크는 다음의 5단계의 과정을 거쳐서 제작되었습니다:\"]},{t:\"ol\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"초기 자료 수집\"]},\": \",{t:\"a\",a:{href:\"https://arxiv.org/abs/2412.03775\"},c:[\"WithdrarXiv\"]},\"와 \",{t:\"a\",a:{href:\"https://pubpeer.com/\"},c:[\"PubPeer\"]},\"에서 오류가 보고된 논문을 선별합니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"자동 필터링\"]},\": GPT-4o 모델을 이용하여 모호성이 없고 명확한 오류만을 필터링합니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"저자의 오류 인정 확인\"]},\": 논문의 저자가 직접 인정한 오류만을 데이터셋에 포함시킵니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"휴먼 어노테이션\"]},\": 휴먼 어노테이터가 명시성과 중대성을 확인하여 추가적으로 오류를 점검합니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"데이터 정규화\"]},\": 최종적으로 OCR 기술을 통해 PDF에서 텍스트와 이미지 데이터를 정규화하여 오류의 명확한 검증이 가능하도록 처리합니다.\"]}]}]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"p\",a:{},c:[\"위와 같은 과정을 거쳐서 만들어진 SPOT은 다음의 10개의 리서치 도메인의 논문을 커버하면서 6가지 유형의 오류를 다루고 있습니다:\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Research Domains\"]},\": Mathematics, Physics, Biology, Chemistry, Materials Science, Medicine, Environmental Science, Engineering, Computer Science, Multidisciplinary\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Error Types\"]},\": Equation / proof, Figure duplication, Data inconsistency, Statistical Reporting, Reagent Identity, Experiment Setup\",{t:\"strong\",a:{},c:[\" \"]}]}]}]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/LFoHG1IrrpihOi7KQpUFJRLBjuI.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"p\",a:{},c:[\"SPOT은 모델의 과학 논문 오류 탐지 및 검증 성능을 평가하기 위해 다음의 세 가지 주요 평가 메트릭을 활용합니다:\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Precision\"]},\": 모델이 지적한 오류 중 실제로 정답으로 확인된 오류의 비율.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Recall\"]},\": SPOT 데이터셋에서 인간이 확인한 모든 실제 오류 중에서 모델이 얼마나 많은 오류를 정확히 발견했는가를 측정.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"pass@K\"]},\": 모델이 논문 한 편에 대해 K회 오류 탐지를 시도했을 때, 적어도 한 번이라도 실패 오류를 찾아내는가를 평가하는 지표.\"]}]}]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-3\"},c:[{t:\"strong\",a:{},c:[\"현재 Multi-modal Model은 오류를 잘 SPOT할 수 있을까?\"]}]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/EWOKJSqoMXjVcrkyMqQhO31w7g.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"p\",a:{},c:[\"여러 proprietary 및 open-source multi-modal model들에 대한 SPOT 벤치마크 평가를 통해 다음과 같은 분석들을 확인하였습니다.\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"상당한 난이도\"]},\": open-source model들은 multi-modal 환경에서 거의 0점에 가까운 성능을 보여주며 오류 탐지에 어려움을 겪었고, text-only 환경에서는 multi-modal 환경보다는 조금 더 높은 성능을 보여줬습니다. 또한 proprietary model들도 o3와 Gemini-2.5-Pro를 제외하고는 거의 0점에 가까운 성능을 보여주며 오류 탐지에 어려움이 있는 모습을 보여줍니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"모델의 Multi-modal 환경 활용\"]},\": 논문을 이해하는데에는 글 뿐만 아니라 이미지도 중요하게 작용합니다. 하지만 결과를 살펴보면 대부분의 모델들이 multi-modal 환경보다 text-only 환경에서 더 뛰어난 성능을 보여주는 것을 확인할 수 있습니다. 이는 대부분의 모델들이 오류 탐지를 진행하는데 이미지 정보를 제대로 활용하지 못하고 있다는 것을 암시하며, 오직 o3와 Gemini-2.5-Pro 만이 text-only 환경에서 multi-modal 환경보다 떨어지는 성능을 보여주며 이미지 정보를 제대로 참고하고 있다는 것을 확인할 수 있습니다.\"]}]}]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/5u4r0E379Jeuuj0oJjtWTt11xl4.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"p\",a:{},c:[\"추가적으로 각 모델의 confidence와 pass@4 성능을 확인해보면 신기한 결과를 확인할 수 있습니다. 위 그림의 우측 그래프를 보면 o3는 Statistical reporting 오류에서 모든 정답을 맞추고 그 외의 모델들도 상당히 높은 정확도를 보여주고 있지만, 모든 모델의 confidence가 0.5를 넘기지 못하는 것으로 보아 모델이 확신을 가지고 오류를 탐지해낸다기 보다는 찍어서 맞추는 경우에 더 가깝다는 것을 보여줍니다. \"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-4\"},c:[{t:\"strong\",a:{},c:[\"마치며..\"]}]},{t:\"p\",a:{},c:['LLM은 이제 단순히 컴퓨터 과학 분야뿐 아니라 다양한 과학 도메인에서도 강력한 성능을 보이고 있습니다. 이에 따라 가설을 세우고 실험을 진행하는 AI Co-Scientist 역할뿐 아니라 연구의 무결성을 검증하는 역할 또한 점점 더 중요해지고 있습니다. 원라인에이아이 연구팀에서 발표한 \"When AI Co-Scientists Fail: SPOT-a Benchmark for Automated Verification of Scientific Research\" 논문에서는 SPOT 벤치마크를 통해 다양한 언어 모델들이 검증자로서 가진 가능성을 분석하였습니다.']},{t:\"p\",a:{},c:[\"실험 결과는 o3나 Gemini-2.5-Pro와 같은 고성능 모델이 아니라면 multi-modal 데이터를 제대로 활용하기 어렵고, 심지어 이런 고성능 언어 모델들조차 매우 낮은 신뢰도(confidence)로 신뢰할 수 없는 검증 결과를 내놓는다는 것을 보여줍니다. 이와 같은 결과는 현재 언어 모델들이 AI Co-Scientist로서 여전히 부족한 점이 많음을 시사합니다. 앞으로 SPOT 벤치마크와 공개된 실험 결과를 바탕으로 AI Co-Scientist가 신뢰할 수 있는 검증자의 역할을 수행할 수 있도록 활발한 후속 연구가 이루어지길 바랍니다.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"References\"]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"📝 Paper: \",{t:\"a\",a:{href:\"https://arxiv.org/abs/2505.11855\"},c:[\"https://arxiv.org/abs/2505.11855\"]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"🤗 Dataset: \",{t:\"a\",a:{href:\"https://huggingface.co/datasets/amphora/SPOT-MetaData\"},c:[\"https://huggingface.co/datasets/amphora/SPOT-MetaData\"]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"🖥️ GitHub: \",{t:\"a\",a:{href:\"https://github.com/guijinSON/SPOT\"},c:[\"https://github.com/guijinSON/SPOT\"]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"🖥️ Blog: \",{t:\"a\",a:{href:\"https://llms-in-science.github.io/spot/\"},c:[\"https://llms-in-science.github.io/spot/\"]}]}]}]}],toc:[{id:\"section-1\",text:\"AI Co-Scientist의 가능성, 검증에서는 어떨까?\"},{id:\"section-2\",text:\"Watch out! I SPOT the errors!\"},{id:\"section-3\",text:\"현재 Multi-modal Model은 오류를 잘 SPOT할 수 있을까?\"},{id:\"section-4\",text:\"마치며..\"}],images:[\"https://framerusercontent.com/images/LFoHG1IrrpihOi7KQpUFJRLBjuI.png\",\"https://framerusercontent.com/images/EWOKJSqoMXjVcrkyMqQhO31w7g.png\",\"https://framerusercontent.com/images/5u4r0E379Jeuuj0oJjtWTt11xl4.png\"],readMins:7},{id:6,page:\"Journal 07\",slug:\"multilingual-test-time-scaling\",kind:\"research\",topic:\"Reasoning\",title:\"Test Time Scaling Works in English, Why It Falters Multilingually\",originalTitle:\"Test Time Scaling Works in English, Why It Falters Multilingually\",summary:\"An investigation of reasoning-time computation across languages.\",language:\"en\",date:\"May 30, 2025\",dateISO:\"2025-05-30\",source:\"https://onelineai.com/en/blog-research/linguistic-generalizability-of-test-time-scaling-in-mathematical-reasoning-en\",path:\"/research/multilingual-test-time-scaling\",nodes:[{t:\"blockquote\",a:{},c:[{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:['This post covers an analysis of the effectiveness of Test-Time Scaling for LLMs in multilingual settings, based on the paper \"',{t:\"a\",a:{href:\"https://arxiv.org/abs/2502.17407\"},c:[\"Linguistic Generalizability of Test-Time Scaling in Mathematical Reasoning\"]},'\". This paper was accepted in the ACL 2025 Main Track.']}]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"This series reviews papers published by OnelineAI. If you have any questions about paper, please contact the \",{t:\"a\",a:{href:\"https://www.onelineai.com/contact\"},c:[\"AI team\"]},\" on OnelineAI.\"]}]}]}]},{t:\"h2\",a:{id:\"section-1\"},c:[{t:\"strong\",a:{},c:[\"Is Test-Time Scaling effective even in multilingual settings?\"]}]},{t:\"p\",a:{},c:[\"In conventional large language models (LLMs), compute scaling during the training phase has often been used to improve performance. However, over time, this approach has reached its limits due to a shortage of high-quality data. In response to this, OpenAI's O-series and DeepSeek's R1 models are turning their attention to compute scaling during the inference phase, or test-time. Recent studies on test-time scaling, which have been gaining traction, highlight that inference-enhancing techniques like Chain-of-Thought (CoT) prompting deliver strong performance, especially in tasks involving mathematics and code generation. Still, it remains unclear whether these methods operate reliably across different languages or in broader domains.\"]},{t:\"p\",a:{},c:[\"The paper\\xa0\",{t:\"em\",a:{},c:['\"Linguistic Generalizability of Test-Time Scaling in Mathematical Reasoning\"']},'\\xa0raises the question, \"Can the cross-lingual training effectiveness demonstrated in traditional train-time scaling also apply to test-time scaling?\" To explore this, the authors propose a benchmark (MCLM) capable of finely evaluating multilingual mathematical reasoning abilities. They assess the performance of representative test-time scaling techniques such as Outcome Reward Modeling (ORM), Process Reward Modeling (PRM), and Budget Forcing (BF) in multilingual settings, investigating how each of these methods performs in diverse linguistic environments.']},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-2\"},c:[{t:\"strong\",a:{},c:[\"What is Test-Time Scaling?\"]}]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Test-time scaling\"]},\"\\xa0is a technique used not only during the training phase but also at inference time to enhance a model's performance. It is particularly applied to challenging tasks such as complex mathematical reasoning. The paper focuses on commonly used Test-Time Scaling methods:\\xa0\",{t:\"strong\",a:{},c:[\"Budget Forcing (BF)\"]},\",\\xa0\",{t:\"strong\",a:{},c:[\"Outcome Reward Model (ORM)\"]},\", and\\xa0\",{t:\"strong\",a:{},c:[\"Process Reward Model (PRM)\"]},\".\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/r7II1qJW3V1jQ1VCLhBWdXenhs.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Budget Forcing (BF)\"]},\": This method was first proposed in \",{t:\"a\",a:{href:\"https://arxiv.org/abs/2501.19393\"},c:[\"s1\"]},\". It strictly constrains or extends the computational budget during the inference phase, encouraging the model to engage in deeper reasoning. For example, if the model exceeds or falls short of the designated budget, it is prompted to either conduct additional inference or conclude more quickly, aiming to improve overall performance.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Outcome Reward Modeling (ORM)\"]},\": ORM is a type of reward model that assigns scores based on the model's final responses. During inference, the model generates multiple candidate answers and selects the one with the highest score as the final answer.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Process Reward Modeling (PRM)\"]},\": Unlike ORM, PRM evaluates the response generation process step-by-step. It guides the inference process progressively through a reward model while the answer is being generated. At each inference step, multiple candidates are explored and the model is steered toward the most optimal path.\"]}]}]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-3\"},c:[{t:\"strong\",a:{},c:[\"Multilingual Competition Level Math (MCLM)\"]}]},{t:\"p\",a:{},c:[\"A widely used multilingual math benchmark has been \",{t:\"a\",a:{href:\"https://arxiv.org/abs/2210.03057\"},c:[\"MGSM\"]},\", a translated version of the original GSM8K. However, MGSM lacks strong discriminatory power as a benchmark because the original GSM8K mainly consists of elementary-level arithmetic problems, and modern models have already achieved very high performance on it. This indicates a need for a new multilingual math benchmark that goes beyond general math word problems and reaches the level of difficulty found in math competitions.\"]},{t:\"p\",a:{},c:[\"MCLM is a multilingual mathematical reasoning benchmark proposed to address this gap. It consists of competition-level problems translated into 55 languages. The following table presents general statistics for each subset of this benchmark:\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/GydTkAOrB4PhpSGgWT7dB5PGsk.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"MT-MATH100\"]},\": 100 problems randomly sampled from the MATH-500 benchmark and translated into 55 languages using GPT-4o\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"MT-AIME2024\"]},\": All problems from the AIME2024 benchmark translated into 55 languages using GPT-4o\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"MT-IMO\"]},\": Out of 114 IMO problems from 2006 to 2024, 27 problems (excluding proof or image-based ones) were collected along with their official translations in 38 languages\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"MT-MO\"]},\": Math Olympiad problems from various countries were collected, covering problems in 11 different languages\"]}]}]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-4\"},c:[{t:\"strong\",a:{},c:[\"다국어 환경에서의 Test-Time Scaling 파헤치기!\"]}]},{t:\"p\",a:{},c:[\"In the MCLM benchmark, we applied three test-time scaling methods—Budget Forcing (BF), Outcome Reward Model (ORM), and Process Reward Model (PRM)—to examine whether performance remains consistent across languages. To evaluate the model's accuracy in a multilingual setting, we used the following two metrics:\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Accuracy\"]},\": Measures the problem-solving accuracy across different language models.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Cross-Lingual Consistency\"]},\": Assessed by calculating Fleiss' kappa to determine whether the model can solve the same problem in different languages. Fleiss' kappa is a metric used to evaluate the agreement among multiple annotators, and in this context, it is used to measure whether the language model produces consistent outputs across different languages.\"]}]}]},{t:\"h4\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"Outcome Reward Modeling (ORM)\"]}]},{t:\"p\",a:{},c:[\"We evaluated performance by generating K responses per question (with K ∈ {2, 4, 8}) using the Qwen2.5-Math-1.5B and 7B models, then selecting the highest-scoring response through ORM. As a result, in the MT-MATH100 subset, performance consistently improved as K increased. However, in the more challenging MT-AIME2024 subset, there was no significant performance improvement in languages other than English. These findings suggest that while ORM serves as an effective scaling strategy in English settings, it struggles to produce high-quality candidates in other languages, leading to limited performance gains.\"]},{t:\"h4\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"Process Reward Modeling (PRM)\"]}]},{t:\"p\",a:{},c:[\"We evaluated the model by generating candidate responses at each step using the Qwen2.5-Math-1.5B model and selecting the candidate with the highest score using the Qwen2.5-Math-PRM 7B and 72B reward models. While the PRM method showed a slight improvement in average performance as the number of steps and candidates increased, there were no clear improvements in performance variance or consistency across languages.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/wrjTgRsxs0EoByTseBwOoGwnQ.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"h4\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"ORM vs. PRM\"]}]},{t:\"p\",a:{},c:[\"Despite both ORM and PRM showing unstable performance improvements and low Fleiss' kappa values in a multilingual environment, ORM demonstrates superior average performance compared to PRM, as seen in the image below. This can be attributed to PRM loading the reward model at every step, resulting in higher latency when given the same inference FLOPs. In the image below, the blue line represents PRM, and the green line represents ORM.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/OY6LiKZ3Hlq5Uv0sDolop3A95I.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"h4\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"Budget Forcing (BF)\"]}]},{t:\"p\",a:{},c:['To examine how the model\\'s multilingual mathematical reasoning capabilities change through BF (presumably \"Backbone Fine-tuning\"), we evaluated models trained under the following settings:']},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Qwen2.5-Math-1.5B + SFT\"]},\": Trained for 3 epochs using 50K randomly selected samples from the OpenR1-220K dataset.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Qwen2.5-Math-1.5B + SFT w/ Translated Data\"]},\": Trained on a dataset where the English samples used in the Qwen2.5-Math-1.5B + SFT training were translated into 14 target languages. Only the question and answer were translated into the target language, while the reasoning trace remained in English.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"DeepSeek-R1-1.5B + SFT w/ Translated Data (MR-1)\"]},\": The DeepSeek-R1-1.5B model was trained using the same translated dataset used for the previous setting. Observing a performance drop with longer training, only 0.5 epochs were used to mitigate this issue.\"]}]}]},{t:\"p\",a:{},c:[\"Based on the evaluation of these models, we derived the following insights:\"]},{t:\"ol\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"Training with translated data does not yield significant performance improvements compared to using English-only data.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"When training with multilingual data, the model shows slightly lower initial accuracy but improves as training progresses.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"Despite being trained on a small amount of data, DeepSeek-R1 shows rapid performance improvement, suggesting that reasoning models benefit more quickly from multilingual data. This indicates that the model’s inherent self-correction ability becomes enhanced in a multilingual setting.\"]}]}]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/XKkeyQ8qhzLfShmWEXfyR3gAKE.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"p\",a:{},c:[\"Using the models in question, experiments were conducted with various BF settings of {2048, 4096, 8192}. In English, there was a consistent improvement in performance, but for most other languages, the performance gains were limited. In fact, as the BF increased from 2048 to 8192, the average performance only improved by about 1.9%.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/L3sDUxyvbtD6DGj8IGQi8kw2b0.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-5\"},c:[{t:\"strong\",a:{},c:[\"Conclusion\"]}]},{t:\"p\",a:{},c:[\"The evaluation results of the three test-time scaling methods used in MCLM — Outcome Reward Modeling (ORM), Process Reward Modeling (PRM), and Budget Forcing (BF) — clearly demonstrated that while these methods lead to significant performance improvements in English environments, their effectiveness does not generalize well to other languages. These findings highlight the limitations of test-time scaling approaches for LLMs in multilingual settings and underscore the need for future research to focus on enhancing multilingual generalization capabilities.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"References\"]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"📝 Paper: \",{t:\"a\",a:{href:\"https://arxiv.org/abs/2502.17407\"},c:[\"https://arxiv.org/abs/2502.17407\"]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"🤗 Dataset: \",{t:\"a\",a:{href:\"https://huggingface.co/datasets/amphora/MCLM\"},c:[\"https://huggingface.co/datasets/amphora/MCLM\"]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"🖥️ GitHub: \",{t:\"a\",a:{href:\"https://github.com/gauss5930/MCLM\"},c:[\"https://github.com/gauss5930/MCLM\"]}]}]}]}],toc:[{id:\"section-1\",text:\"Is Test-Time Scaling effective even in multilingual settings?\"},{id:\"section-2\",text:\"What is Test-Time Scaling?\"},{id:\"section-3\",text:\"Multilingual Competition Level Math (MCLM)\"},{id:\"section-4\",text:\"다국어 환경에서의 Test-Time Scaling 파헤치기!\"},{id:\"section-5\",text:\"Conclusion\"}],images:[\"https://framerusercontent.com/images/r7II1qJW3V1jQ1VCLhBWdXenhs.png\",\"https://framerusercontent.com/images/GydTkAOrB4PhpSGgWT7dB5PGsk.png\",\"https://framerusercontent.com/images/wrjTgRsxs0EoByTseBwOoGwnQ.png\",\"https://framerusercontent.com/images/OY6LiKZ3Hlq5Uv0sDolop3A95I.png\",\"https://framerusercontent.com/images/XKkeyQ8qhzLfShmWEXfyR3gAKE.png\",\"https://framerusercontent.com/images/L3sDUxyvbtD6DGj8IGQi8kw2b0.png\"],readMins:6},{id:7,page:\"Journal 08\",slug:\"multilingual-test-time-scaling-ko\",kind:\"research\",topic:\"Reasoning\",title:\"영어에선 통하던 Test Time Scaling, 다국어에선 왜 힘이 빠질까?\",originalTitle:\"영어에선 통하던 Test Time Scaling, 다국어에선 왜 힘이 빠질까?\",summary:\"다국어 환경에서 추론 시간 확장의 효과와 한계를 분석합니다\",language:\"ko\",date:\"May 30, 2025\",dateISO:\"2025-05-30\",source:\"https://onelineai.com/en/blog-research/linguistic-generalizability-of-test-time-scaling-in-mathematical-reasoning-ko\",path:\"/research/multilingual-test-time-scaling-ko\",nodes:[{t:\"blockquote\",a:{},c:[{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:['\"',{t:\"a\",a:{href:\"https://arxiv.org/abs/2502.17407\"},c:[\"Linguistic Generalizability of Test-Time Scaling in Mathematical Reasoning\"]},'\" 논문을 기반으로 다국어 환경에서 LLM의 Test-Time Scaling의 효과에 대해 분석하는 내용을 다루고 있습니다. 해당 논문은 ACL 2025',{t:\"a\",a:{href:\"https://2024.aclweb.org/\"},c:[\" \"]},\"Main Track에 게재되었습니다.\"]}]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"이 시리즈는 원라인에이아이에서 발표한 논문을 리뷰합니다. 논문과 관련해서 궁금한 내용이 있다면 \",{t:\"a\",a:{href:\"https://www.onelineai.com/contact\"},c:[{t:\"strong\",a:{},c:[\"원라인에이아이 AI팀\"]}]},\"에게 문의주시기 바랍니다.\"]}]}]}]},{t:\"h2\",a:{id:\"section-1\"},c:[{t:\"strong\",a:{},c:[\"Test-Time Scaling, multilingual 환경에서도 효과적일까?\"]}]},{t:\"p\",a:{},c:[\"기존 LLM에서는 성능 향상을 위해 학습 단계에서의 compute scaling 방법이 많이 사용되었지만, 시간이 지남에 따라 양질의 데이터의 부족으로 인해 학습에서의 compute scaling이 한계에 다다르고 있습니다. 이러한 상황에서 OpenAI의 O-series와 DeepSeek의 R1 모델들은 새로운 접근법으로 추론 단계(test-time)의 compute scaling에 주목하고 있습니다. 최근 각광받고 있는 test-time scaling 관련 연구들은 Chain-of-Thought (CoT) 프롬프팅과 같은 추론 강화 기법들이 특히 수학 및 코드 작성과 같은 태스크에서 효과적인 성능을 보여주고 있지만, 이러한 방법론들이 언어적 다양성이나 도메인 확장성에서도 안정적으로 작동할지는 여전히 불명확한 상태입니다.\"]},{t:\"p\",a:{},c:['\"Linguistic Generalizability of Test-Time Scaling in Mathematical Reasoning\" 논문에서는 이러한 의문점을 기반으로 \"기존 train-time scaling에서 입증된 cross-lingual training의 효과가 test-time scaling에서도 동일한 효과를 보여줄까?\" 라는 질문을 던집니다. 이를 탐구하기 위해 다국어 수학 추론 능력을 세밀하게 평가할 수 있는 벤치마크(MCLM)를 제안하고, Outcome Reward Modeling(ORM), Process Reward Modeling(PRM), Budget Forcing(BF)와 같은 대표적인 test-time scaling 기법의 성능을 다국어 환경에서 평가하며 각각의 test-time scaling 방법론이 다국어 환경에서 어떤 모습을 보여주는지 파헤칩니다.']},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-2\"},c:[{t:\"strong\",a:{},c:[\"Test-Time Scaling이 무엇일까?\"]}]},{t:\"p\",a:{},c:[\"Test-time scaling은 학습 과정에서뿐만 아니라, 추론 시점에서도 모델의 성능을 확장하기 위한 기법으로, 특히 복잡한 수학적 추론과 같은 난제에 적용되고 있습니다. 논문에서는 Test-Time Scaling 방법 중 많이 사용되는 Budget Forcing(BF), Outcome Reward Model(ORM), Process Reward Model(PRM)에 대해 중점적으로 살펴보았습니다.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/r7II1qJW3V1jQ1VCLhBWdXenhs.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Budget Forcing (BF): \"]},\"이 방법은 \",{t:\"a\",a:{href:\"https://arxiv.org/abs/2501.19393\"},c:[\"s1\"]},\"에서 처음 제안되 방법론으로 추론 단계에서 주어진 계산량을 엄격히 제한하거나 확장하며 모델이 더욱 심도 깊은 사고를 하도록 유도합니다. 예를 들어서, 모델이 지정된 예산(budget)을 초과하거나 미달하는 경우, 추가적인 추론을 유도하거나 빠르게 결론짓도록 요구함으로써 성능 향상을 꾀하는 방식입니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Outcome Reward Model (ORM)\"]},\": ORM은 모델이 생성한 최종 응답을 기반으로 점수를 매기는 reward model의 한 종류로, 추론 과정에서 여러 개의 후보 답안을 생성한 후, 이 중 가장 높은 점수를 받은 답안을 최종적으로 선택하는 방식입니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Process Reward Model (PRM)\"]},\": PRM은 ORM과 달리 모델이 생성하는 응답을 단계별로 평가하면서 추론 과정 자체를 점진적으로 보상 모델을 통해 지도하며 답안을 생성하는 방법입니다. 각 추론 단계에서 여러 후보를 탐색하고, 이 중 최적의 방향으로 모델이 진전하도록 유도합니다. \"]}]}]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-3\"},c:[{t:\"strong\",a:{},c:[\"Multilingual Competition Level Math (MCLM)\"]}]},{t:\"p\",a:{},c:[\"기존의 대표적인 다국어 수학 벤치마크로는 GSM8K를 번역한 \",{t:\"a\",a:{href:\"https://arxiv.org/abs/2210.03057\"},c:[\"MGSM\"]},\"이 널리 사용되어 왔습니다. 그러나 MGSM은 원본 벤치마크인 GSM8K가 초등학교 사칙연산 수준의 쉬운 문제로 구성되어 있어서, 최신 모델들이 이미 매우 높은 성능을 기록하며 벤치마크로서의 변별력이 현저히 떨어진 상황입니다. 따라서 이러한 다국어 수학 벤치마크의 부재는 일반적인 math word problem을 넘어 경시대회 수준의 난이도를 갖춘 새로운 다국어 수학 벤치마크가 필요하다는 점을 시사합니다.\"]},{t:\"p\",a:{},c:[{t:\"a\",a:{href:\"https://huggingface.co/datasets/amphora/MCLM\"},c:[\"MCLM\"]},\"은 이러한 다국어 수학 벤치마크의 부재 문제를 해결하기 위해 제안된 다국어 수학 추론 벤치마크로 55개의 언어로 이루어진 경진대회 수준 문제들로 구성되었습니다. 다음의 표는 해당 벤치마크의 각 서브셋에 대한 전반적인 통계치를 보여줍니다:\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/GydTkAOrB4PhpSGgWT7dB5PGsk.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"MT-MATH100\"]},\": MATH-500 벤치마크에서 100개의 문제를 랜덤하게 샘플링하고, \",{t:\"code\",a:{},c:[\"gpt-4o\"]},\"를 활용하여 55개의 언어로 번역 진행\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"MT-AIME2024\"]},\": AIME2024 벤치마크의 전체 문제에 대해 \",{t:\"code\",a:{},c:[\"gpt-4o\"]},\"를 활용하여 55개의 언어로 번역 진행\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"MT-IMO\"]},\": 2006년부터 2024년 까지의 IMO 문제 114개 중 증명 또는 이미지 기반 문제를 제외한 27개의 문제에 대해 38개 언어의 공식 번역 문제들을 수집\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"MT-MO\"]},\": 각국에서 진행되는 수학 올림피아드 문제들을 모두 수집하여 총 11개의 언어에 대한 수학 올림피아드 문제 수집\"]}]}]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-4\"},c:[{t:\"strong\",a:{},c:[\"다국어 환경에서의 Test-Time Scaling 파헤치기!\"]}]},{t:\"p\",a:{},c:[\"MCLM 벤치마크에서 3가지 test-time scaling 방법론 Budget Forcing (BF), Outcome Reward Model (ORM), Process Reward Model (PRM)을 적용하여 언어 간의 성능이 일관되게 유지되는지 실험하였습니다. 이때 다국어 환경에서 모델의 정확도를 확인하기 위해 다음의 두 가지 지표에서 평가를 진행했습니다.\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Accuracy\"]},\": 서로 다른 언어 모델 간의 문제 풀이 정확도를 평가\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Cross-Lingual Consistency\"]},\": 모델이 똑같은 문제에 대해서 서로 다른 언어로 풀이할 수 있는지 확인하기 위해 Fleiss' kappa를 측정하였습니다. Fleiss kappa는 다수의 annotator 간에 얼마나 일치하는 annotation을 하는지 평가하기 위한 지표인데, 이를 다국어 환경에서 언어 모델이 각 언어들에 대해 동일한 출력을 뽑아내는가를 평가하기 위해 사용했습니다.\"]}]}]},{t:\"h4\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"Outcome Reward Modeling (ORM)\"]}]},{t:\"p\",a:{},c:[\"Qwen2.5-Math-1.5B와 7B 모델을 사용해 문제당 K (with {2, 4, 8})개의 응답 생성 후, ORM을 통해 가장 높은 점수를 얻은 응답을 선택하는 방식으로 평가하였습니다. 그 결과, MT-MATH100 서브셋에서는 K의 값이 오름에 따라 성능이 꾸준히 향상되었으나, 더 어려운 서브셋인 MT-AIME2024의 경우 영어를 제외한 다른 언어에서는 유의미한 성능 향상을 보이지 않았습니다. 이러한 결과는 ORM이 영어 환경에서는 유의미한 scaling 전략이지만, 다른 언어에서는 고품질 candidate를 생성하는데 어려움을 겪으며 제한된 성능 향상을 보여준다고 유추할 수 있습니다.\"]},{t:\"h4\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"Process Reward Modeling (PRM)\"]}]},{t:\"p\",a:{},c:[\"Qwen2.5-Math-1.5B 모델을 사용해 응답 생성 과정에서 매 단계마다 후보 응답을 생성하고 Qwen2.5-Math-PRM 7B & 72B reward model을 활용하여 가장 높은 점수를 얻는 후보 응답을 선택하는 방식으로 평가하였습니다. PRM 방법 역시 step의 수와 candidate의 수를 늘림에 따라 평균 성능이 약간 증가하였으나, 성능 편차나 언어 간의 일관성에서는 뚜렷한 개선점이 없었습니다. \"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/wrjTgRsxs0EoByTseBwOoGwnQ.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"h4\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"ORM vs. PRM\"]}]},{t:\"p\",a:{},c:[\"ORM과 PRM 모두 다국어 환경에서 불안정한 성능 향상과 낮은 Fleiss' kappa 값을 보여줬음에도 불구하고, 다음의 이미지에서 확인할 수 있듯이 ORM은 평균 성능 측면에서 PRM을 능가하는 성능을 보여줍니다. 이는 똑같은 inference FLOPs가 주어졌을 때, PRM은 매 스텝마다 reward model을 불러오며 더 높은 latency를 만들어내기 때문이라고 볼 수 있습니다. 하단 이미지에서 파란선은 PRM, 초록선은 ORM을 의미합니다.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/OY6LiKZ3Hlq5Uv0sDolop3A95I.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"h4\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"Budget Forcing (BF)\"]}]},{t:\"p\",a:{},c:[\"BF를 통한 모델의 다국어 수학 추론 능력 변화를 확인하기 위해 다음과 같은 세팅으로 학습시킨 모델을 대상으로 평가를 진행하였습니다:\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Qwen2.5-Math-1.5B + SFT\"]},\": OpenR1-220K 데이터셋에서 50K 샘플을 랜덤하게 뽑아서 3 epoch 학습\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Qwen2.5-Math-1.5B + SFT w/ Translated Data\"]},\": Qwen2.5-Math-1.5B + SFT 모델 학습에 사용한 데이터셋에서 영어 샘플을 14개의 언어로 번역하여 학습 데이터셋으로 사용. 이때 reasoning trace 자체는 영어로 남기고 question과 answer만 target language로 번역 진행.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"DeepSeek-R1-1.5B + SFT w/ Translated Data (MR-1)\"]},\": Qwen2.5-Math-1.5B + SFT w/ Translated Data에서 사용한 학습 데이터셋으로DeepSeek-R1-1.5B 모델에서 학습. 학습을 길게 진행할 경우 성능 저하가 발생하는 것을 관찰하여, 이를 완화하기 위해 0.5 epoch 학습만 진행.\"]}]}]},{t:\"p\",a:{},c:[\"해당 모델들에 대한 평가 결과로 다음과 같은 분석 결과를 도출하였습니다:\"]},{t:\"ol\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"번역 데이터를 사용한 학습은 English-only 데이터를 사용하는 것에 비해 그리 큰 성능 향상을 보이지는 않음.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"multilingual 데이터로 학습 시 초반에는 조금 낮은 정확도를 보여주지만, 학습이 진행됨에 따라 오르는 것을 확인.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"DeepSeek-R1은 매우 소수의 데이터로 학습했음에도 불구하고 빠른 성능 향상을 보여주는데, 이는 reasoning model이 더 빠르게 multilingual data에서 베네핏을 가진다는 것을 보여줌. 이는 모델이 이미 가지고 있는 self-correction 능력이 multilingual setting으로 증대되는 것으로 볼 수 있음.\"]}]}]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/XKkeyQ8qhzLfShmWEXfyR3gAKE.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"p\",a:{},c:[\"해당 모델들을 활용하여 {2048, 4096, 8192}의 다양한 BF 세팅에서 실험을 한 결과, 영어에서는 꾸준하게 성능 향상을 보여줬지만, 대부분의 다른 언어에 대해서는 제한된 성능 향상이 일어났습니다. 실제로도 BF가 2048에서 8192로 늘어남에 따라 평균 성능은 고작 1.9% 정도밖에 증가하지 않았습니다. \"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/L3sDUxyvbtD6DGj8IGQi8kw2b0.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-5\"},c:[{t:\"strong\",a:{},c:[\"마치며..\"]}]},{t:\"p\",a:{},c:[\"MCLM에서의 Outcome Reward Modeling(ORM), Process Reward Modeling(PRM), Budget Forcing(BF), 이 세 가지 test-time scaling 방법론의 평가 결과는 test-time scaling 방법들이 영어 환경에서는 뚜렷한 성능 향상을 가져오지만, 그 성과가 다른 언어로 일반화되는 데 한계가 있음을 분명히 보여주었습니다. 이러한 결과는 다국어 환경에서 LLM의 test-time scaling 방법의 한계를 명확히 드러내고, 향후 연구가 다국어 일반화 능력 향상에 집중해야 할 필요성을 강조하였습니다. \"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"References\"]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"📝 Paper: \",{t:\"a\",a:{href:\"https://arxiv.org/abs/2502.17407\"},c:[\"https://arxiv.org/abs/2502.17407\"]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"🤗 Dataset: \",{t:\"a\",a:{href:\"https://huggingface.co/datasets/amphora/MCLM\"},c:[\"https://huggingface.co/datasets/amphora/MCLM\"]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"🖥️ GitHub: \",{t:\"a\",a:{href:\"https://github.com/gauss5930/MCLM\"},c:[\"https://github.com/gauss5930/MCLM\"]}]}]}]}],toc:[{id:\"section-1\",text:\"Test-Time Scaling, multilingual 환경에서도 효과적일까?\"},{id:\"section-2\",text:\"Test-Time Scaling이 무엇일까?\"},{id:\"section-3\",text:\"Multilingual Competition Level Math (MCLM)\"},{id:\"section-4\",text:\"다국어 환경에서의 Test-Time Scaling 파헤치기!\"},{id:\"section-5\",text:\"마치며..\"}],images:[\"https://framerusercontent.com/images/r7II1qJW3V1jQ1VCLhBWdXenhs.png\",\"https://framerusercontent.com/images/GydTkAOrB4PhpSGgWT7dB5PGsk.png\",\"https://framerusercontent.com/images/wrjTgRsxs0EoByTseBwOoGwnQ.png\",\"https://framerusercontent.com/images/OY6LiKZ3Hlq5Uv0sDolop3A95I.png\",\"https://framerusercontent.com/images/XKkeyQ8qhzLfShmWEXfyR3gAKE.png\",\"https://framerusercontent.com/images/L3sDUxyvbtD6DGj8IGQi8kw2b0.png\"],readMins:9},{id:8,page:\"Journal 09\",slug:\"korean-multiplication\",kind:\"research\",topic:\"Evaluation\",title:\"구구단으로 파헤쳐보는 한국어 LLM\",originalTitle:\"구구단으로 파헤쳐보는 한국어 LLM\",summary:\"한국어 언어 모델의 추론 특성을 구구단 평가로 살펴봅니다\",language:\"ko\",date:\"Apr 24, 2025\",dateISO:\"2025-04-24\",source:\"https://onelineai.com/en/blog-research/%EA%B5%AC%EA%B5%AC%EB%8B%A8-%ED%95%9C%EA%B5%AD%EC%96%B4-llm\",path:\"/research/korean-multiplication\",nodes:[{t:\"h2\",a:{id:\"section-1\"},c:[{t:\"strong\",a:{},c:[\"다양한 한국어 LLM의 등장\"]}]},{t:\"p\",a:{},c:[\"2024년 8월, LG AI Research에서 공개한 한국어-영어 bi-lingual 언어 모델 EXAONE-3.0 이후로 후속 모델인 EXAONE-3.5, 카카오의 kanana, 그리고 네이버의 HyperClovaX Seed까지 다양한 한국어 오픈소스 모델이 등장하였습니다. 해당 모델들은 KMMLU, HAERAE-Bench, CLIcK, KoBEST 등 다양한 한국어 벤치마크에서 뛰어난 성능을 보여주며 한국어 오픈소스 언어 모델 시장의 중요한 모델들로 자리 잡고 있습니다. \"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/ubzCOJr8xoyYI7fPG2ykqENlpIM.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"p\",a:{},c:[\"현재 가장 많이 사용되는 한국어 벤치마크인 KMMLU의 2년전 첫 공개 당시에는 HyperClova X-Large가 53.4점으로 최고점을 기록했지만, 현재는 불과 3B 규모의 HyperCLOVAX Seed 모델이 48.47점을 기록했습니다. 따라서 다양한 한국어 벤치마크에서 좋은 성능을 보여준 모델들(EXAONE, Kanana, HyperCLOVAX Seed)을 대상으로 LLM의 간단한 평가 방법으로 많이 사용되는 2-digit multiplcation problem, 즉 '구구단 문제'를 통해 모델의 성능을 직접 측정해 보았습니다.\"]},{t:\"h2\",a:{id:\"section-2\"},c:[{t:\"strong\",a:{},c:[\"구구단을 외자!\"]}]},{t:\"p\",a:{},c:['구구단 문제는 1부터 100까지의 곱셈(예: 7x9)을 \"\\\\boxed{}\" 형식으로 답하도록 요청한 뒤, 총 10,000개 조합의 응답을 정규표현식으로 파싱해 정답 여부(정답=1, 오답=0)를 이진 행렬로 구성하여 분석하였습니다. 해당 결과를 녹색(정답)\\xb7적색(오답) 컬러맵의 히트맵으로 시각화하여, 모델이 영어 입력과 한국어 입력에서 각각 어떤 분포로 답을 맞추는지 확인해보았고, 실험에 사용된 모델과 환경은 다음과 같습니다:']},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"모델\"]}]},{t:\"ol\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Naver\"]},\": \",{t:\"a\",a:{href:\"https://huggingface.co/naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-1.5B\"},c:[\"HyperCLOVAX-SEED-Text-Instruct-1.5B\"]},\" (vllm inference 이슈로 인해 1.5B 사용)\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Kakao\"]},\": \",{t:\"a\",a:{href:\"https://huggingface.co/kakaocorp/kanana-nano-2.1b-instruct\"},c:[\"kanana-nano-2.1b-instruct\"]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"LG AI Research\"]},\": \",{t:\"a\",a:{href:\"https://huggingface.co/LGAI-EXAONE/EXAONE-3.5-2.4B-Instruct\"},c:[\"EXAONE-3.5-2.4B-Instruct\"]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Alibaba\"]},\": \",{t:\"a\",a:{href:\"https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct\"},c:[\"Qwen2.5-1.5B-Instruct\"]}]}]}]},{t:\"h5\",a:{},c:[\"실험 환경\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"temperature\"]},\": 0.0\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"max_tokens\"]},\": [10, 20, 40, 80]\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"vllm==0.8.4\"]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"code\"]},\": \",{t:\"a\",a:{href:\"https://gist.github.com/gauss5930/2e7b1940a356cc696170b44c38fdd82c\"},c:[\"Link\"]}]}]}]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"실험 결과\"]}]},{t:\"p\",a:{},c:[\"실험 결과는 다음의 히트맵과 표와 같습니다. 히트맵에서 확인할 수 있듯이 대부분의 모델이 작은 숫자의 곱셈에서는 어느 정도 정확한 계산 결과를 보여주지만, 높은 숫자의 곱셈에서는 점점 성능이 낮아지는 것을 볼 수 있습니다.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"'구구단 문제' 결과 (max_tokens=10)\"]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/GJ79SuSY1K9qbQhq8ZVOhnPkKc.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"'구구단 문제' 성능 (max_tokens=10)\"]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/gU2jRBMWG0Kr643MTKYP9U5SX0.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"추가 실험 결과\"]}]},{t:\"p\",a:{},c:[\"본 실험은 max_tokens를 10으로 설정하여 진행하였지만, 낮은 output token limit으로 인해 발생할 수 있는 답변 잘림 문제 등을 해결하기 위해 20, 40, 80 토큰에 대한 실험도 추가적으로 진행하였습니다. \"]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"[max_tokens=20 결과]\"]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/CkVhU9nk63H46AzHB1l6HA4DLg.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"[max_tokens=40 결과]\"]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/qXiYRqLzFxnLBIhcRYpxe3HKU.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"[max_tokens=80 결과]\"]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/oXrjj4IyXRvR8itToDv1qfZ9Z0.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"max_tokens에 따른 성능 변화 분석 (English)\"]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/qrXgVvwOnGzFCgiy65JqanSM.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"h2\",a:{id:\"section-3\"},c:[{t:\"strong\",a:{},c:[\"한국어 LLM에 대한 분석\"]}]},{t:\"p\",a:{},c:[\"한국어 LLM 각각에 대한 '구구단 문제' 실험 결과 분석은 다음과 같습니다:\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"HyperCLOVAX Seed\"]},\": 영어에서는 괜찮은 성능을 보여주지만, 한국어에서는 굉장히 안 좋은 성능을 보여줍니다. (한국어-영어의 성능 차이가 큼) \"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Kanana\"]},\": 한국어에서는 괜찮은 성능을 보여주지만, 영어에서는 굉장히 안 좋은 성능을 보여줍니다. (한국어-영어의 성능 차이가 큼)\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"EXAONE\"]},\": 영어와 한국어 모두에서 굉장히 robust한 성능을 보여줍니다. (한국어-영어의 성능 차이가 별로 없음)\"]}]}]},{t:\"p\",a:{},c:[\"서로 다른 한국어 LLM은 각각이 상이한 특징을 보였는데, 이를 통해 각 모델의 특징을 조금이나마 확인할 수 있었습니다. 여기에 추가적으로 실험 결과를 통해 다음과 같은 특징들 또한 확인할 수 있었습니다:\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"특징 1\"]},\": 모델들이 공통적으로 제곱수(대각행렬)와 10의 자릿수 곱셈에 대해서는 높은 정확도를 보여줍니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"특징 2\"]},\": 대각행렬을 기준으로 완전 대칭이 아닌 것으로 보아 똑같은 곱셈이라도 피연산자의 위치에 따라서 성능이 달라지는 것으로 보입니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"특징 3\"]},\": Kanana의 경우, max_tokens가 늘어남에 따라 정확도가 크게 오르는 것을 확인할 수 있습니다. 이는 해당 모델이 영어로 답변을 출력할 때, 한국어보다 좀 더 많은 토큰을 뱉음으로써 답변이 잘리는 문제를 해결할 수 있었던 것으로 보입니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"특징 4\"]},\": HyperCLOVAX Seed의 경우, 일의 자리수가 '7'인 행과 열에서 오답이 자주 발생하는 것을 확인할 수 있습니다. \"]}]}]},{t:\"h2\",a:{id:\"section-4\"},c:[{t:\"strong\",a:{},c:[\"마치며..\"]}]},{t:\"p\",a:{},c:[\"최근 EXAONE, Kanana, HyperCLOVAX Seed 등 다양한 한국어 오픈소스 LLM이 잇따라 공개되면서, 국내 인공지능 생태계 전반에 큰 활력을 불어넣고 있습니다. 이처럼 한국어에 최적화된 모델들이 지속적으로 등장하고 있다는 사실은, 단순한 기술적 진보를 넘어 한국어 LLM 연구의 새로운 전환점을 예고하는 반가운 흐름입니다.\"]},{t:\"p\",a:{},c:[\"특히 그동안 영어 중심으로 발전해온 LLM 연구 흐름 속에서, 한국어라는 언어의 고유한 특성과 구조를 깊이 있게 반영한 모델들의 등장은 국내 연구자들에게 더 많은 기회를 제공하고 있습니다. 각 모델들이 저마다의 철학과 기술적 접근을 기반으로 다양한 활용 사례를 만들어내고 있는 만큼, 이러한 흐름이 단발적인 현상에 그치지 않고, 보다 지속적이고 체계적인 연구로 이어져 한국어 LLM 생태계가 더욱 견고해지기를 바랍니다. 또한 저희 원라인에아이에서도 한국어 LLM의 사용성을 평가할 수 있는 다양한 벤치마크와 평가 방법들을 끊임없이 만들어나가겠습니다!\"]}],toc:[{id:\"section-1\",text:\"다양한 한국어 LLM의 등장\"},{id:\"section-2\",text:\"구구단을 외자!\"},{id:\"section-3\",text:\"한국어 LLM에 대한 분석\"},{id:\"section-4\",text:\"마치며..\"}],images:[\"https://framerusercontent.com/images/ubzCOJr8xoyYI7fPG2ykqENlpIM.png\",\"https://framerusercontent.com/images/GJ79SuSY1K9qbQhq8ZVOhnPkKc.png\",\"https://framerusercontent.com/images/gU2jRBMWG0Kr643MTKYP9U5SX0.png\",\"https://framerusercontent.com/images/CkVhU9nk63H46AzHB1l6HA4DLg.png\",\"https://framerusercontent.com/images/qXiYRqLzFxnLBIhcRYpxe3HKU.png\",\"https://framerusercontent.com/images/oXrjj4IyXRvR8itToDv1qfZ9Z0.png\",\"https://framerusercontent.com/images/qrXgVvwOnGzFCgiy65JqanSM.png\"],readMins:5},{id:9,page:\"Journal 10\",slug:\"won-financial-model\",kind:\"research\",topic:\"Finance\",title:\"KRX 금융 언어 모델 경진대회와 한국어 금융 언어 모델 ₩ON 이야기\",originalTitle:\"KRX 금융 언어 모델 경진대회와 한국어 금융 언어 모델 ₩ON 이야기\",summary:\"KRX 경진대회와 한국어 금융 모델 개발에 대한 기록\",language:\"ko\",date:\"Apr 10, 2025\",dateISO:\"2025-04-10\",source:\"https://onelineai.com/en/blog-research/won\",path:\"/research/won-financial-model\",nodes:[{t:\"h2\",a:{id:\"section-1\"},c:[{t:\"strong\",a:{},c:[\"KRX 금융 언어 모델 경진대회와 ₩ON 모델 제작 이야기\"]}]},{t:\"p\",a:{},c:[\"인공지능의 발전은 모든 산업을 빠르게 변화시키고 있습니다. 그중에서도 금융 분야는 AI 기술이 빠르게 확산되고 있는 대표적인 산업군 중 하나입니다. 이러한 빠른 발전에 발맞춰서 KRX는 금융 특화 언어 모델의 개발과 성능 향상을 촉진하고, 이를 통해 더욱 정확하고 효율적인 금융 정보 분석 및 예측 서비스를 제공하고자 KRX 금융 언어 모델 경진대회를 개최하였습니다.\"]},{t:\"p\",a:{},c:[\" 해당 대회에서 참가자들이 제출한 금융 특화 언어 모델을 평가하기 위해 KRX와 OnelineAI가 공동 개발한 'KRX-Bench'를 이용하였습니다. 참가자들은 각자의 방법론을 활용하여 모델을 학습시키고 제출하면, KRX-Bench에서 제출 모델의 금융 능력을 평가하여 \",{t:\"a\",a:{href:\"https://krxbench.koscom.co.kr/home/leaderboard\"},c:[\"리더보드\"]},\"를구축하는 방식으로 대회가 진행되었습니다. 약 2개월 동안 진행된 경진대회를 통해 기록된 수많은 정보들을 활용하여 강력한 금융 언어 모델을 만들기 위한 다양한 노하우가 담긴 \",{t:\"a\",a:{href:\"https://arxiv.org/abs/2503.17963\"},c:[{t:\"strong\",a:{},c:[\"Technical Review\"]}]},\"와 해당 노하우들을 기반으로 제작된 한국어 금융 언어 모델인 \",{t:\"a\",a:{href:\"https://huggingface.co/KRX-Data/WON-Reasoning\"},c:[{t:\"strong\",a:{},c:[\"₩ON\"]}]},\"에 대해서 자세히 소개하겠습니다.\"]},{t:\"h2\",a:{id:\"section-2\"},c:[{t:\"strong\",a:{},c:[\"KRX 금융 언어 모델 경진대회에 대하여..\"]}]},{t:\"p\",a:{},c:[\"본 경진대회는 한국 금융 분야에서 최초로 시도된 공개적인 LLM 평가 리더보드 구축 및 모델 평가 경진대회입니다. 약 2개월간 진행된 이번 대회는 233개의 팀이 참가 등록을 완료하였으며, 총 1,119개의 모델이 제출되는 등 놀라운 성과를 기록하며 마무리되었습니다. 또한 참가팀의 절반 이상인 52.5%가 기업 참가자였으며, 그 외에는 모두 학계 관계자인 것으로 보아 금융 LLM 분야가 다양한 도메인으로부터 관심을 받고 있다는 것을 보여주었습니다. 여기에 추가적으로 현재까지도 약 600개 이상의 모델이 HuggingFace를 통해 공개적으로 활용 가능하며, 한국 금융 NLP 연구를 위한 소중한 자산으로 남아있습니다.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/gB0CmM5Jlsqyxk7r3qZTv8tXX6c.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"p\",a:{},c:[\" 경진대회는 예선과 본선, 총 2가지 단계로 나눠서 진행되었으며 각 단계에서는 다음과 같이 서로 다른 방향성으로 진행하였습니다:\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"예선\"]},\": MCQA 유형의 비교적 쉬운 질문으로 구성된 5개의 서브셋을 포함하는 벤치마크 평가. (재무회계, 금융시장, 주가 예측, 국내 기업, 금융 에이전트)\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"본선\"]},\": MCQA 및 Instruction-Response 유형의 비교적 어려운 질문으로 구성된 3개의 서브셋을 포함하는 벤치마크 평가. (재무회계, 금융시장, 금융 질의응답)\"]}]}]},{t:\"p\",a:{},c:[\" 경진대회에 사용된 벤치마크는 다음과 같이 6가지 서브셋에서 총 5,500개 이상의 평가 항목으로 구성되었습니다. 이때 각 서브셋에 대한 자세한 설명은 다음과 같습니다:\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"재무회계(MCQA)\"]},': 대학 시험에서 출제된 4지선다형 객관식 문제로 구성되어 있으며, 예선에서는 4개, 본선에서는 8개의 보기로 확장하였습니다. 문제의 난이도를 높이기 위해 임베딩 기반의 유사 문제 혼합과 \"위의 보기 중 없음\"과 같은 규칙 기반의 보기 교체와 같은 데이터 증강 기법을 사용하였습니다.']}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"금융시장(MCQA)\"]},\": 재무회계 서브셋과 유사한 방식으로 구성되었으며, 주로 한국의 금융 시스템과 관련 법률에 대한 이해를 평가하는 시험 문제를 기반으로 하고 있습니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"주가 예측(MCQA)\"]},\": 한국 주식 시장의 최신 데이터를 사용하여 OHLCV(시가, 고가, 저가, 종가, 거래량)와 같은 기본 데이터를 무작위로 추출한 후, 기술적 지표(예: 최근 5일, 10일, 15일, 20일, 25일, 30일 동안의 변화율 등)를 계산하여 제공하였습니다. 모델은 주가가 향후 상승할지 하락할지를 이진 분류 문제로 예측해야 하며, 기본적인 모멘텀 또는 평균 회귀 신호 탐지 능력 등을 평가합니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"국내 기업(MCQA)\"]},\": KRX-Bench를 직접 사용하여 구성하였으며, GPT-4o를 활용하여 한국 상장 기업의 연례 보고서를 바탕으로 자동 생성된 객관식 문제로 이루어져 있습니다. 이 서브셋은 기업의 제품 제공, 재무 정책, 사업 전략과 같은 특정 분야의 지식을 평가합니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"금융 에이전트(MCQA)\"]},\": 실제 금융 데이터를 기반으로 자동화된 금융 에이전트로 기능할 수 있는 능력을 평가합니다. 모델에게 CSV 파일과 특정 정보를 추출하거나 데이터를 처리하는 코드 작업 지시를 제공하고, 모델은 여러 변형된 출력 옵션 중에서 올바른 답안을 선택해야 합니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"금융 질의응답(Instruction-Response)\"]},\": 객관식 평가의 한계를 보완하기 위해, 실제 응용 시나리오를 더 잘 반영할 수 있도록 보다 도전적인 100개의 Instruction-Response 프롬프트로 구성된 서브셋입니다. 법적 추론을 포함한 KRX-Bench의 일부, HRM8K의 고급 수학 문제, 대학원 수준의 금융공학 및 계량경제학 시험 문제에서 추출한 질문으로 구성되었으며, 정답 평가를 위해 GPT-4o를 활용한 LLM-as-a-Judge 평가 방식을 사용하였습니다.\"]}]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/D6V0TZuueNOmJRwlwNw9bJoU.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"h2\",a:{id:\"section-3\"},c:[{t:\"strong\",a:{},c:[\"경진대회 결과 분석\"]}]},{t:\"p\",a:{},c:[\"대회에 참가한 모든 팀들의 노하우가 담긴 결과를 토대로 다음과 같이 데이터셋 구성, 상위 모델 방법론 분석 등을 진행하였습니다.\"]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"데이터 수집 분석\"]}]},{t:\"p\",a:{},c:[\"이번 경진대회에서는 참가팀들이 라이선스 문제가 없는 금융 관련 콘텐츠를 중점적으로 수집했습니다. 가장 많이 활용된 출처는 정부(go.kr) 및 비영리 기관(or.kr)의 도메인이었으며, 주요 출처로는 한국거래소(KRX), 금융위원회(FSC), 한국은행(BOK) 등이 있었습니다. 참가자들이 중점적으로 수집한 데이터 소스는 다음의 표와 같습니다.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/vSaKBQ68D40TGTEG2iJv8dFQSo.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"예선 상위 모델 분석\"]}]},{t:\"p\",a:{},c:[\"예선 상위 10개 팀은 모두 Qwen2.5-7B-Instruct 모델을 기반으로 SFT를 적용하여 높은 성과를 기록했습니다. 특히, 국내 기업 카테고리에서는 0.51에서 0.94로 가장 큰 성능 개선이 이루어졌지만, 재무회계, 금융 시장 카테고리에서는 상대적으로 미미한 성과 향상이 관찰되었습니다.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/NOWsC2DpY8pHEm4OACsAgVZSYg.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\" 또한 대회 진행이 흐름에 따라 전반적으로 모델들이 성능이 우상향을 보여주는 것 또한 볼 수 있었습니다.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/9KkVKyFEE0jBLrSqZZp7COb0xyA.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"본선 상위 모델 분석\"]}]},{t:\"p\",a:{},c:[\"본선에서는 더 복잡하고 다단계적인 훈련 전략이 사용되었습니다. 예를 들어 Shibainu 팀은 난이도 기반 커리큘럼 학습을 적용했고, 추가로 DPO(Direct Preference Optimization)를 활용해 모델의 응답 품질을 정교하게 개선했습니다. Hi-Q와 overfit-brothers 팀 역시 DPO와 KTO 같은 고급 미세조정 방식을 사용했습니다. 특히 Hi-Q 팀의 분석 결과, 지속적 사전학습인 Continual Pre-training(CPT)과 SFT을 결합한 경우가 단순 SFT보다 약 2.7점 더 높은 성과를 보였으며, 이는 금융 특화 모델의 성능을 높이는데 CPT의 유효성을 시사합니다.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/qArvM4JD5FMwI00SxrLx6tcaCiA.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"h4\",a:{},c:[\"종합 평가 및 시사점\"]},{t:\"p\",a:{},c:[\"경진대회의 결과는 금융 분야에서 우수한 성능을 보이는 한국어 특화 언어 모델 개발에 있어 효과적인 데이터 수집과 복합적인 미세조정 전략이 중요함을 강조했습니다. 데이터의 품질 관리와 라이선스 문제 회피가 중요하며, 이러한 과정을 통해 고품질의 학습 데이터를 확보할 수 있었습니다. 또한, 복잡한 모델 학습 전략이 어려운 현실적인 금융 문제를 잘 해결하는 데 효과적이라는 사실이 입증되었습니다. 이는 앞으로의 금융 특화 모델 개발 및 평가 과정에서 중요한 지침으로 활용될 수 있을 것으로 기대됩니다.\"]},{t:\"h2\",a:{id:\"section-4\"},c:[{t:\"strong\",a:{},c:[\"오픈 한국어 금융 언어 모델: ₩ON\"]}]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"모델 학습 데이터셋 제작\"]}]},{t:\"p\",a:{},c:[\"₩ON 모델의 학습은 앞서 설명한 '경진대회 결과 분석 - 데이터 수집 분석'의 내용을 기반으로 대회 기간 동안 수집된 약 20만 개의 데이터 중 중복 제거와 품질 검사를 통해 최종 선별된 86,000여 개의 고품질 한국어 금융 데이터를 기반으로 진행하였습니다. 수집된 raw data에 대해서는 GPT-4o 및 Qwen2.5-72B-Instruct 모델을 활용하여 객관식(MCQA) 또는 지시문-응답(Instruction-Response) 형식으로 변환하였습니다. 마지막으로 이렇게 제작된 데이터셋에 대해 추가적인 필터링을 진행하여 최종 데이터셋을 구축하였습니다. 자세한 데이터 수집 및 처리 과정은 다음과 같습니다:\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"데이터 수집\"]},\": 경진대회 중 HuggingFace에 제출된 200,000개 이상의 데이터 중 MinHash 알고리즘과 정규식 필터링을 통해 선별된 86,000여 개의 Instruction 데이터셋을 구축\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"추론 응답\"]},\": DeepSeek-R1 모델을 사용하여 생성된 응답과 함께 영어 및 한국어 온라인 자료에서 수집한 Prompt-Response 쌍으로 보완\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"검증\"]},\": GPT-4o를 LLM-as-a-Judge로 활용한 검증 프로세스 및 자동 품질 검사를 통해 데이터의 무결성과 정확성을 강화\"]}]}]},{t:\"p\",a:{},c:[\"해당 데이터셋에 대한 보다 자세한 내용은 HuggingFace에 공개되어 있는 \",{t:\"a\",a:{href:\"https://huggingface.co/datasets/KRX-Data/Won-Instruct\"},c:[\"Won-Instruct\"]},\" 데이터셋을 참고해주시길 바랍니다.\"]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"모델 학습 방법론\"]}]},{t:\"p\",a:{},c:[\"₩ON 모델 학습에는 최신 연구 흐름과 경진대회 우승 팀들이 사용한 학습 전략에서 영감을 받아 2단계 학습 과정을 적용하였습니다:\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Supervised Fine-Tuning (SFT)\"]},\": 금융 추론 작업과 관련된 모델의 초기 행동을 조정하는데 초점을 맞췄으며, DeepSeek-R1 모델로 생성한 상세한 응답과 세심하게 선정된 프롬프트를 사용하여 학습하였습니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Direct Preference Optimization (DPO)\"]},\": SFT 진행 후, 모델의 과도한 생각 과정 또는 일부 질문에 대한 잘못된 해석과 같은 원치 않는 행동을 완화하기 위해 DPO를 활용하여 모델을 추가 학습하였습니다. 이때 모델의 출력과 DeepSeek-R1의 출력을 비교함으로써 선호도 데이터를 제작하였습니다.\"]}]}]},{t:\"p\",a:{},c:[\"이와 같은 과정을 통해 학습된 ₩ON 모델은 다음과 같은 특징을 가지고 있습니다:\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Base Model\"]},\": Qwen2.5-Math-7B-Instruct\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Language\"]},\": Korean, English\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Model Size\"]},\": 7B\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Inference:\"]}]},{t:\"p\",a:{},c:[\"₩ON은 두 단계의 구조적 추론 과정을 거칩니다.\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Thinking Stage\"]},\": 모델은 `<think>` 및 `</think>` 태그 내에서 자신의 추론 과정을 명시적으로 보여줍니다. 이는 투명성을 높이고, 사용자들이 ₩ON의 결론 도출 방식을 이해하는 데 도움을 줍니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Solution Stage\"]},\": 추론 진행 후, 모델은 결론을 `<solution>` 및 `</solution>` 태그 내에서 명확하고 간결하게 요약하여 제시합니다.\"]}]}]}]}]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"모델 성능 평가 및 분석\"]}]},{t:\"p\",a:{},c:[\"₩ON 모델을 본선에 사용된 벤치마크로 평가한 결과 다음의 표와 같은 결과가 나왔습니다. ₩ON 모델은 재무회계 및 금융 질의응답 서브셋에서 특히 우수한 성능을 보이고, 이에 반해 금융시장 서브셋에서는 조금 약한 모습을 보여줬습니다. 이는 모델이 학습 과정에서 추론 위주의 데이터에 더 초점을 맞췄기 때문에 수학적, 논리적 추론 능력을 요구하는 복잡한 질문들에 대해 강력한 성능을 보이는 반면, 사실적이고 도메인 특화된 지식을 요구하는 질문들에 대해서는 상대적으로 약한 성능을 보이는 것으로 해석할 수 있습니다.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/wvRTKBrVKT3jcQOEwpVqUMLLgc.png\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"이러한 결과를 통해 추론 위주의 학습 방법이 특정 금융 문제 해결에는 효과적인 한편, 향후 금융 도메인 지식 및 사실 기반 데이터를 균형 있게 추가 학습시키는 것이 성능의 종합적 개선을 위해 필수적이라는 것을 보여줍니다.\"]},{t:\"h2\",a:{id:\"section-5\"},c:[{t:\"strong\",a:{},c:[\"마치며..\"]}]},{t:\"p\",a:{},c:[\"KRX 금융 언어 모델 경진대회는 금융 분야에서의 한국어 LLM 개발과 평가의 새로운 가능성을 확인시켜준 중요한 계기가 되었습니다. 특히, 이 과정에서 축적된 데이터와 학습 노하우는 앞으로의 금융 특화 모델 연구에 값진 토대가 될 것으로 기대됩니다. ₩ON 모델의 사례는 금융 AI의 실무적 활용 가능성을 보여주는 동시에, 향후 더 고도화된 금융 지식과 추론 역량을 갖춘 모델이 지속적으로 개발되어야 한다는 과제를 남겼습니다. 앞으로도 이러한 도전이 지속되어 한국 금융 시장이 글로벌 AI 경쟁력을 확보하는 데 기여할 수 있기를 바랍니다.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"References\"]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"🖥️ \",{t:\"strong\",a:{},c:[\"Won-Reasoning\"]},\": \",{t:\"a\",a:{href:\"https://huggingface.co/KRX-Data/WON-Reasoning\"},c:[\"https://huggingface.co/KRX-Data/WON-Reasoning\"]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"💾 \",{t:\"strong\",a:{},c:[\"Won-Instruct\"]},\": \",{t:\"a\",a:{href:\"https://huggingface.co/datasets/KRX-Data/Won-Instruct\"},c:[\"https://huggingface.co/datasets/KRX-Data/Won-Instruct\"]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"📝 \",{t:\"strong\",a:{},c:[\"Won: Establishing Best Practices for Korean Financial NLP\"]},\": \",{t:\"a\",a:{href:\"https://arxiv.org/abs/2503.17963\"},c:[\"https://arxiv.org/abs/2503.17963\"]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"📝 \",{t:\"strong\",a:{},c:[\"KRX Technical Review\"]},\": Coming Soon!\"]}]}]}],toc:[{id:\"section-1\",text:\"KRX 금융 언어 모델 경진대회와 ₩ON 모델 제작 이야기\"},{id:\"section-2\",text:\"KRX 금융 언어 모델 경진대회에 대하여..\"},{id:\"section-3\",text:\"경진대회 결과 분석\"},{id:\"section-4\",text:\"오픈 한국어 금융 언어 모델: ₩ON\"},{id:\"section-5\",text:\"마치며..\"}],images:[\"https://framerusercontent.com/images/gB0CmM5Jlsqyxk7r3qZTv8tXX6c.png\",\"https://framerusercontent.com/images/D6V0TZuueNOmJRwlwNw9bJoU.png\",\"https://framerusercontent.com/images/vSaKBQ68D40TGTEG2iJv8dFQSo.png\",\"https://framerusercontent.com/images/NOWsC2DpY8pHEm4OACsAgVZSYg.png\",\"https://framerusercontent.com/images/9KkVKyFEE0jBLrSqZZp7COb0xyA.png\",\"https://framerusercontent.com/images/qArvM4JD5FMwI00SxrLx6tcaCiA.png\",\"https://framerusercontent.com/images/wvRTKBrVKT3jcQOEwpVqUMLLgc.png\"],readMins:10},{id:10,page:\"Journal 11\",slug:\"ko-r1-1-5b\",kind:\"research\",topic:\"Models\",title:\"Reaching New Heights: GPT-4o Level with Ko-R1-1.5B\",originalTitle:\"Reaching New Heights: GPT-4o Level with Ko-R1-1.5B\",summary:\"A development note on a compact Korean reasoning model.\",language:\"en\",date:\"Jan 29, 2025\",dateISO:\"2025-01-29\",source:\"https://onelineai.com/en/blog-research/ko-r1-preview\",path:\"/research/ko-r1-1-5b\",nodes:[{t:\"p\",a:{},c:[\"Deepseek-R1 has been a hot topic in the past days. We've been working on something pretty similar. Figure 1 shows a model response from a model that was trained a week ago (two days before the drop of R1), and you can see pretty similar behaviors of self-correcting. This was an extension of our works on OLV (Olaf-With-Verification). \"]},{t:\"p\",a:{},c:[\"(link to model is as the bottom of the page)\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/Nmuud8urGZAVmo3drOi2zMvhY.png?width=1973&height=992\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"Now, with Deepseek-R1 being public and our work outdated, we've raced to bring the first Korean re-implementation of R1. Earlier this month, in our paper (\",{t:\"a\",a:{href:\"https://www.arxiv.org/abs/2501.02448\"},c:[\"Ko et al., 2025\"]},\"), we discussed a three-step generation process where we use models to initially understand (or solve) in English and then translate the results into Korean. We found this applicable to R1 as well since it also responds in two steps: <think> and <solution>. Most of the solving is actually done in the <think> step, which is followed by a <solution> step that restates the answer for better readability. Accordingly, in our implementation of Ko-R1, we first make our model think in English and respond in Korean.\"]},{t:\"p\",a:{},c:[\"Here is a sample response:\"]},{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"pre\",a:{},c:[{t:\"span\",a:{},c:[\"Q\"]},\": $\",{t:\"span\",a:{},c:[\"2\"]},\"^\",{t:\"span\",a:{},c:[\"8\"]},\"=\",{t:\"span\",a:{},c:[\"4\"]},\"^\",{t:\"span\",a:{},c:[\"x\"]},\"$\",{t:\"span\",a:{},c:[\"일\"]},\" \",{t:\"span\",a:{},c:[\"때\"]},{t:\"span\",a:{},c:[\",\"]},\" $\",{t:\"span\",a:{},c:[\"x\"]},\"$\",{t:\"span\",a:{},c:[\"의\"]},\" \",{t:\"span\",a:{},c:[\"값은\"]},\" \",{t:\"span\",a:{},c:[\"얼마입니까\"]},\"?\\n\",{t:\"span\",a:{},c:[\"Response\"]},\" \",{t:\"span\",a:{},c:[\"by\"]},\" \",{t:\"span\",a:{},c:[\"ko\"]},\"-\",{t:\"span\",a:{},c:[\"r1\"]},\"-\",{t:\"span\",a:{},c:[\"preview\"]},\"-\",{t:\"span\",a:{},c:[\"1.5\"]},{t:\"span\",a:{},c:[\"B\"]},\":\\n<\",{t:\"span\",a:{},c:[\"think\"]},\">\\n\",{t:\"span\",a:{},c:[\"Okay\"]},{t:\"span\",a:{},c:[\",\"]},\" \",{t:\"span\",a:{},c:[\"so\"]},\" \",{t:\"span\",a:{},c:[\"I\"]},\" \",{t:\"span\",a:{},c:[\"need\"]},\" \",{t:\"span\",a:{},c:[\"to\"]},\" \",{t:\"span\",a:{},c:[\"solve\"]},\" \",{t:\"span\",a:{},c:[\"the\"]},\" \",{t:\"span\",a:{},c:[\"equation\"]},\" \",{t:\"span\",a:{},c:[\"2\"]},\"^\",{t:\"span\",a:{},c:[\"8\"]},\" = \",{t:\"span\",a:{},c:[\"4\"]},\"^\",{t:\"span\",a:{},c:[\"x\"]},\" \",{t:\"span\",a:{},c:[\"and\"]},\" \",{t:\"span\",a:{},c:[\"find\"]},\" \",{t:\"span\",a:{},c:[\"the\"]},\" \",{t:\"span\",a:{},c:[\"value\"]},\" \",{t:\"span\",a:{},c:[\"of\"]},\" \",{t:\"span\",a:{},c:[\"x\"]},\". \",{t:\"span\",a:{},c:[\"Let\"]},\" \",{t:\"span\",a:{},c:[\"me\"]},\" \",{t:\"span\",a:{},c:[\"think\"]},\" \",{t:\"span\",a:{},c:[\"about\"]},\" \",{t:\"span\",a:{},c:[\"how\"]},\" \",{t:\"span\",a:{},c:[\"to\"]},\" \",{t:\"span\",a:{},c:[\"approach\"]},\" \",{t:\"span\",a:{},c:[\"this\"]},\" \",{t:\"span\",a:{},c:[\"step\"]},\" \",{t:\"span\",a:{},c:[\"by\"]},\" \",{t:\"span\",a:{},c:[\"step\"]},\". \\\\\\\\\",{t:\"span\",a:{},c:[\"n\"]},\"\\\\\\\\\",{t:\"span\",a:{},c:[\"nFirst\"]},{t:\"span\",a:{},c:[\",\"]},\" \",{t:\"span\",a:{},c:[\"I\"]},\" \",{t:\"span\",a:{},c:[\"remember\"]},\" \",{t:\"span\",a:{},c:[\"that\"]},\" \",{t:\"span\",a:{},c:[\"when\"]},\" \",{t:\"span\",a:{},c:[\"dealing\"]},\" \",{t:\"span\",a:{},c:[\"with\"]},\" \",{t:\"span\",a:{},c:[\"exponential\"]},\" \",{t:\"span\",a:{},c:[\"equations\"]},{t:\"span\",a:{},c:[\",\"]},\" \",{t:\"span\",a:{},c:[\"it\"]},{t:\"span\",a:{},c:[\"'s often helpful to express both sides of the equation with the same base if possible. That way, I can set the exponents equal to each other because if a^b = c^d, then b must equal d if a and c are the same. So, let'\"]},{t:\"span\",a:{},c:[\"s see if I can rewrite 4 as a power of 2. Since 4 is 2 squared, right? Because 2 times 2 is 4. So, 4 is 2^2.\\\\\\\\n\\\\\\\\nSo substituting that into the original equation, we have:\\\\\\\\n\\\\\\\\n2^8 = (2^2)^x\\\\\\\\n\\\\\\\\nNow, using the exponentiation rule (a^m)^n = a^(m*n), which means multiplying the exponents. Applying that here, the right side becomes 2^(2*x). So now the equation is:\\\\\\\\n\\\\\\\\n2^8 = 2^(2x)\\\\\\\\n\\\\\\\\nSince the bases are the same (both are powers of 2), their exponents must be equal for the equality to hold. Therefore, I can set the exponents equal to each other:\\\\\\\\n\\\\\\\\n8 = 2x\\\\\\\\n\\\\\\\\nTo solve for x, I just divide both sides by 2:\\\\\\\\n\\\\\\\\nx = 8 / 2\\\\\\\\nx = 4\\\\\\\\n\\\\\\\\nWait, let me check that again. If x is 4, then 4^4 would be 256, and 2^8 is also 256. Yeah, that works out. But maybe there's another way to verify?\\\\\\\\n\\\\\\\\nAlternatively, I could take logarithms on both sides. Let's try that method to confirm. Taking the natural log or common log of both sides. Let's use natural logs for this example.\\\\\\\\n\\\\\\\\nTaking ln of both sides:\\\\\\\\n\\\\\\\\nln(2^8) = ln(4^x)\\\\\\\\n\\\\\\\\nUsing the logarithm property that ln(a^b) = b*ln(a):\\\\\\\\n\\\\\\\\n8*ln(2) = x*ln(4)\\\\\\\\n\\\\\\\\nThen solving for x:\\\\\\\\n\\\\\\\\nx = (8*ln(2)) / ln(4)\\\\\\\\n\\\\\\\\nBut since 4 is 2^2, ln(4) is ln(2^2) = 2*ln(2). Substituting back in:\\\\\\\\n\\\\\\\\nx = (8*ln(2)) / (2*ln(2)) = 8/2 = 4\\\\\\\\n\\\\\\\\nSame result. So that confirms it. Both methods give x = 4. \\\\\\\\n\\\\\\\\nAnother way to look at it: Maybe converting both numbers to base 10? Although that might not be necessary here. Let's see. 2^8 is 256, and 4^x. If I compute 4^x until I reach 256, starting from x=1: 4, 16, 64, 256. So that's 4^4=256. Yep, so x=4. \\\\\\\\n\\\\\\\\nI guess all these different approaches—expressing both sides with the same base, taking logarithms, and iterative checking—all lead to the same answer. It seems pretty straightforward once you recognize that 4 is a power of 2. The key was to express everything in terms of the same base before equating the exponents. \\\\\\\\n\\\\\\\\nJust to recap: Original equation is 2^8 equals 4^x. Express 4 as 2^2, substitute into the equation, simplify using exponent rules, set exponents equal, solve for x. All steps check out, and verifying through multiple methods gives confidence in the solution.\\n</think>\\n<solution>\\\\\\\\n\\\\\\\\\\\\\\\\(2^8 = 4^x\\\\\\\\\\\\\\\\)을 풀기 위해, 우리는 양쪽을 같은 밑으로 표현합니다. \\\\\\\\\\\\\\\\(4\\\\\\\\\\\\\\\\)를 \\\\\\\\\\\\\\\\(2^2\\\\\\\\\\\\\\\\)로 다시 씁니다:\\\\\\\\n\\\\\\\\n\\\\\\\\\\\\\\\\[\\\\\\\\n2^8 = (2^2)^x\\\\\\\\n\\\\\\\\\\\\\\\\]\\\\\\\\n\\\\\\\\n지수 법칙 \\\\\\\\\\\\\\\\((a^m)^n = a^{mn}\\\\\\\\\\\\\\\\)을 사용하여 오른쪽을 단순화하면:\\\\\\\\n\\\\\\\\n\\\\\\\\\\\\\\\\[\\\\\\\\n2^8 = 2^{2x}\\\\\\\\n\\\\\\\\\\\\\\\\]\\\\\\\\n\\\\\\\\n밑이 같으므로 지수는 같아야 합니다:\\\\\\\\n\\\\\\\\n\\\\\\\\\\\\\\\\[\\\\\\\\n8 = 2x\\\\\\\\n\\\\\\\\\\\\\\\\]\\\\\\\\n\\\\\\\\n\\\\\\\\\\\\\\\\(x\\\\\\\\\\\\\\\\)를 구하면:\\\\\\\\n\\\\\\\\n\\\\\\\\\\\\\\\\[\\\\\\\\nx = \\\\\\\\\\\\\\\\frac{8}{2} = 4\\\\\\\\n\\\\\\\\\\\\\\\\]\\\\\\\\n\\\\\\\\n검증:\\\\\\\\n- \\\\\\\\\\\\\\\\(4^4 = 256\\\\\\\\\\\\\\\\)이고 \\\\\\\\\\\\\\\\(2^8 = 256\\\\\\\\\\\\\\\\)이므로 해가 맞음을 확인할 수 있습니다.\\\\\\\\n- 로그를 사용한 검증도 결과를 확인합니다.\\\\\\\\n\\\\\\\\n따라서, \\\\\\\\\\\\\\\\(x\\\\\\\\\\\\\\\\)의 값은 \\\\\\\\\\\\\\\\(\\\\\\\\\\\\\\\\boxed{4}\\\\\\\\\\\\\\\\)입니다.\\n</solution>\"]}]}]}]}]}]}]}]}]}]},{t:\"p\",a:{},c:[\"The biggest issue is performance, right? We evaluated on three subsets of HRM8K: GSM8K, Math, and Omni Math. I am planning a few updates on the KSM subset, so it has been left out. I will try to push the updates soon. I use the following setting for evaluation:\"]},{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"pre\",a:{},c:[]}]}]}]}]}]}]}]}]},{t:\"p\",a:{},c:[\"You can see the models outperforming models of similar size and even those that are way bigger. It only struggles in GSM8K, which is similar to findings from \",{t:\"a\",a:{href:\"https://arxiv.org/abs/2412.21187\"},c:[\"Chen et al., 2025\"]},\", where these o1-like LLMs trained to generate longer sequences tend to overthink in easier questions.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/KAwbhmmEGZxMzzQCPLo1y3i2hfk.png?width=3844&height=832\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"We release the model (1.5B) and evaluation result files. \",{t:\"br\",a:{},c:[]},\"Bigger and better models will come soon.\"]},{t:\"p\",a:{},c:[\"Enjoy the model here -> \",{t:\"a\",a:{href:\"https://huggingface.co/OLAIR/ko-r1-1.5b-preview\"},c:[\"[Link to Model]\"]}]}],toc:[],images:[\"https://framerusercontent.com/images/Nmuud8urGZAVmo3drOi2zMvhY.png?width=1973&height=992\",\"https://framerusercontent.com/images/KAwbhmmEGZxMzzQCPLo1y3i2hfk.png?width=3844&height=832\"],readMins:4},{id:11,page:\"Journal 12\",slug:\"ko-r1-1-5b-ko\",kind:\"research\",topic:\"Models\",title:\"Ko-R1-1.5B, GPT-4o의 경지에 도달하다\",originalTitle:\"Ko-R1-1.5B, GPT-4o의 경지에 도달하다\",summary:\"한국어 추론 모델 Ko-R1-1.5B의 개발 및 평가 기록\",language:\"ko\",date:\"Jan 29, 2025\",dateISO:\"2025-01-29\",source:\"https://onelineai.com/en/blog-research/ko-r1-1-5b-korean\",path:\"/research/ko-r1-1-5b-ko\",nodes:[{t:\"p\",a:{},c:[\"Deepseek-R1 has been a hot topic in the past days. We've been working on something pretty similar. Figure 1 shows a model response from a model that was trained a week ago (two days before the drop of R1), and you can see pretty similar behaviors of self-correcting. This was an extension of our works on OLV (Olaf-With-Verification). \"]},{t:\"p\",a:{},c:[\"최근 며칠 동안 Deepseek-R1이 뜨거운 화제가 되고 있습니다. OnelineAI 연구팀도 꽤 비슷한 작업을 진행해왔는데요, 다음의 그림은 약 일주일 전(Deepseek-R1이 공개되기 이틀 전)에 훈련된 모델의 응답 내용을 보여주고 있습니다. 해당 모델은 연구팀에서 진행했던 OLV (Olaf-With-Verification) 연구의 연장선에서 이루어진 작업으로, 이 모델도 스스로 오류를 수정하는 self-correcting과 유사한 행동을 보이고 있습니다.\"]},{t:\"p\",a:{},c:[\"(모델에 대한 링크는 본 포스팅의 하단을 참고하시길 바랍니다.)\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/Nmuud8urGZAVmo3drOi2zMvhY.png?width=1973&height=992\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"Now, with Deepseek-R1 being public and our work outdated, we've raced to bring the first Korean re-implementation of R1. Earlier this month, in our paper (\",{t:\"a\",a:{href:\"https://www.arxiv.org/abs/2501.02448\"},c:[\"Ko et al., 2025\"]},\"), we discussed a three-step generation process where we use models to initially understand (or solve) in English and then translate the results into Korean. We found this applicable to R1 as well since it also responds in two steps: <think> and <solution>. Most of the solving is actually done in the <think> step, which is followed by a <solution> step that restates the answer for better readability. Accordingly, in our implementation of Ko-R1, we first make our model think in English and respond in Korean.\"]},{t:\"p\",a:{},c:[\"Deepseek-R1의 공개와 함께 연구팀의 작업 내용이 다소 outdate 되었기에, 우리는 한국 최초의 R1 재구현을 빠르게 선보이기로 했습니다. 이달 초 연구팀에서 발표한 논문 (\",{t:\"a\",a:{href:\"https://www.arxiv.org/abs/2501.02448\"},c:[\"Ko et al., 2025\"]},\") 에서는 세 단계로 이루어진 생성 과정에 대해 논의했는데, 먼저 영어로 문제를 이해하거나 해결한 후 그 결과를 한국어로 번역하는 방식이었습니다. 이와 유사하게 R1도 \",{t:\"code\",a:{},c:[\"<think>\"]},\"와 \",{t:\"code\",a:{},c:[\"<solution>\"]},\", 이렇게 두 단계로 응답합니다. 대부분의 문제 해결은 \",{t:\"code\",a:{},c:[\"<think>\"]},\" 단계에서 이루어지며, 그다음 \",{t:\"code\",a:{},c:[\"<solution>\"]},\" 단계에서 가독성을 높이기 위해 답을 다시 정리합니다. 우리는 이러한 R1의 추론 방식에 논문에서 소개한 방법론을 적용해볼 수 있다고 판단하여, 이에 따라 Ko-R1에서도 먼저 영어로 사고한 후, 한국어로 답변을 생성하는 방식을 택했습니다.\"]},{t:\"p\",a:{},c:[\"모델 응답 예시:\"]},{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"pre\",a:{},c:[{t:\"span\",a:{},c:[\"Q\"]},\": $\",{t:\"span\",a:{},c:[\"2\"]},\"^\",{t:\"span\",a:{},c:[\"8\"]},\"=\",{t:\"span\",a:{},c:[\"4\"]},\"^\",{t:\"span\",a:{},c:[\"x\"]},\"$\",{t:\"span\",a:{},c:[\"일\"]},\" \",{t:\"span\",a:{},c:[\"때\"]},{t:\"span\",a:{},c:[\",\"]},\" $\",{t:\"span\",a:{},c:[\"x\"]},\"$\",{t:\"span\",a:{},c:[\"의\"]},\" \",{t:\"span\",a:{},c:[\"값은\"]},\" \",{t:\"span\",a:{},c:[\"얼마입니까\"]},\"?\\n\",{t:\"span\",a:{},c:[\"Response\"]},\" \",{t:\"span\",a:{},c:[\"by\"]},\" \",{t:\"span\",a:{},c:[\"ko\"]},\"-\",{t:\"span\",a:{},c:[\"r1\"]},\"-\",{t:\"span\",a:{},c:[\"preview\"]},\"-\",{t:\"span\",a:{},c:[\"1.5\"]},{t:\"span\",a:{},c:[\"B\"]},\":\\n<\",{t:\"span\",a:{},c:[\"think\"]},\">\\n\",{t:\"span\",a:{},c:[\"Okay\"]},{t:\"span\",a:{},c:[\",\"]},\" \",{t:\"span\",a:{},c:[\"so\"]},\" \",{t:\"span\",a:{},c:[\"I\"]},\" \",{t:\"span\",a:{},c:[\"need\"]},\" \",{t:\"span\",a:{},c:[\"to\"]},\" \",{t:\"span\",a:{},c:[\"solve\"]},\" \",{t:\"span\",a:{},c:[\"the\"]},\" \",{t:\"span\",a:{},c:[\"equation\"]},\" \",{t:\"span\",a:{},c:[\"2\"]},\"^\",{t:\"span\",a:{},c:[\"8\"]},\" = \",{t:\"span\",a:{},c:[\"4\"]},\"^\",{t:\"span\",a:{},c:[\"x\"]},\" \",{t:\"span\",a:{},c:[\"and\"]},\" \",{t:\"span\",a:{},c:[\"find\"]},\" \",{t:\"span\",a:{},c:[\"the\"]},\" \",{t:\"span\",a:{},c:[\"value\"]},\" \",{t:\"span\",a:{},c:[\"of\"]},\" \",{t:\"span\",a:{},c:[\"x\"]},\". \",{t:\"span\",a:{},c:[\"Let\"]},\" \",{t:\"span\",a:{},c:[\"me\"]},\" \",{t:\"span\",a:{},c:[\"think\"]},\" \",{t:\"span\",a:{},c:[\"about\"]},\" \",{t:\"span\",a:{},c:[\"how\"]},\" \",{t:\"span\",a:{},c:[\"to\"]},\" \",{t:\"span\",a:{},c:[\"approach\"]},\" \",{t:\"span\",a:{},c:[\"this\"]},\" \",{t:\"span\",a:{},c:[\"step\"]},\" \",{t:\"span\",a:{},c:[\"by\"]},\" \",{t:\"span\",a:{},c:[\"step\"]},\". \\\\\\\\\",{t:\"span\",a:{},c:[\"n\"]},\"\\\\\\\\\",{t:\"span\",a:{},c:[\"nFirst\"]},{t:\"span\",a:{},c:[\",\"]},\" \",{t:\"span\",a:{},c:[\"I\"]},\" \",{t:\"span\",a:{},c:[\"remember\"]},\" \",{t:\"span\",a:{},c:[\"that\"]},\" \",{t:\"span\",a:{},c:[\"when\"]},\" \",{t:\"span\",a:{},c:[\"dealing\"]},\" \",{t:\"span\",a:{},c:[\"with\"]},\" \",{t:\"span\",a:{},c:[\"exponential\"]},\" \",{t:\"span\",a:{},c:[\"equations\"]},{t:\"span\",a:{},c:[\",\"]},\" \",{t:\"span\",a:{},c:[\"it\"]},{t:\"span\",a:{},c:[\"'s often helpful to express both sides of the equation with the same base if possible. That way, I can set the exponents equal to each other because if a^b = c^d, then b must equal d if a and c are the same. So, let'\"]},{t:\"span\",a:{},c:[\"s see if I can rewrite 4 as a power of 2. Since 4 is 2 squared, right? Because 2 times 2 is 4. So, 4 is 2^2.\\\\\\\\n\\\\\\\\nSo substituting that into the original equation, we have:\\\\\\\\n\\\\\\\\n2^8 = (2^2)^x\\\\\\\\n\\\\\\\\nNow, using the exponentiation rule (a^m)^n = a^(m*n), which means multiplying the exponents. Applying that here, the right side becomes 2^(2*x). So now the equation is:\\\\\\\\n\\\\\\\\n2^8 = 2^(2x)\\\\\\\\n\\\\\\\\nSince the bases are the same (both are powers of 2), their exponents must be equal for the equality to hold. Therefore, I can set the exponents equal to each other:\\\\\\\\n\\\\\\\\n8 = 2x\\\\\\\\n\\\\\\\\nTo solve for x, I just divide both sides by 2:\\\\\\\\n\\\\\\\\nx = 8 / 2\\\\\\\\nx = 4\\\\\\\\n\\\\\\\\nWait, let me check that again. If x is 4, then 4^4 would be 256, and 2^8 is also 256. Yeah, that works out. But maybe there's another way to verify?\\\\\\\\n\\\\\\\\nAlternatively, I could take logarithms on both sides. Let's try that method to confirm. Taking the natural log or common log of both sides. Let's use natural logs for this example.\\\\\\\\n\\\\\\\\nTaking ln of both sides:\\\\\\\\n\\\\\\\\nln(2^8) = ln(4^x)\\\\\\\\n\\\\\\\\nUsing the logarithm property that ln(a^b) = b*ln(a):\\\\\\\\n\\\\\\\\n8*ln(2) = x*ln(4)\\\\\\\\n\\\\\\\\nThen solving for x:\\\\\\\\n\\\\\\\\nx = (8*ln(2)) / ln(4)\\\\\\\\n\\\\\\\\nBut since 4 is 2^2, ln(4) is ln(2^2) = 2*ln(2). Substituting back in:\\\\\\\\n\\\\\\\\nx = (8*ln(2)) / (2*ln(2)) = 8/2 = 4\\\\\\\\n\\\\\\\\nSame result. So that confirms it. Both methods give x = 4. \\\\\\\\n\\\\\\\\nAnother way to look at it: Maybe converting both numbers to base 10? Although that might not be necessary here. Let's see. 2^8 is 256, and 4^x. If I compute 4^x until I reach 256, starting from x=1: 4, 16, 64, 256. So that's 4^4=256. Yep, so x=4. \\\\\\\\n\\\\\\\\nI guess all these different approaches—expressing both sides with the same base, taking logarithms, and iterative checking—all lead to the same answer. It seems pretty straightforward once you recognize that 4 is a power of 2. The key was to express everything in terms of the same base before equating the exponents. \\\\\\\\n\\\\\\\\nJust to recap: Original equation is 2^8 equals 4^x. Express 4 as 2^2, substitute into the equation, simplify using exponent rules, set exponents equal, solve for x. All steps check out, and verifying through multiple methods gives confidence in the solution.\\n</think>\\n<solution>\\\\\\\\n\\\\\\\\\\\\\\\\(2^8 = 4^x\\\\\\\\\\\\\\\\)을 풀기 위해, 우리는 양쪽을 같은 밑으로 표현합니다. \\\\\\\\\\\\\\\\(4\\\\\\\\\\\\\\\\)를 \\\\\\\\\\\\\\\\(2^2\\\\\\\\\\\\\\\\)로 다시 씁니다:\\\\\\\\n\\\\\\\\n\\\\\\\\\\\\\\\\[\\\\\\\\n2^8 = (2^2)^x\\\\\\\\n\\\\\\\\\\\\\\\\]\\\\\\\\n\\\\\\\\n지수 법칙 \\\\\\\\\\\\\\\\((a^m)^n = a^{mn}\\\\\\\\\\\\\\\\)을 사용하여 오른쪽을 단순화하면:\\\\\\\\n\\\\\\\\n\\\\\\\\\\\\\\\\[\\\\\\\\n2^8 = 2^{2x}\\\\\\\\n\\\\\\\\\\\\\\\\]\\\\\\\\n\\\\\\\\n밑이 같으므로 지수는 같아야 합니다:\\\\\\\\n\\\\\\\\n\\\\\\\\\\\\\\\\[\\\\\\\\n8 = 2x\\\\\\\\n\\\\\\\\\\\\\\\\]\\\\\\\\n\\\\\\\\n\\\\\\\\\\\\\\\\(x\\\\\\\\\\\\\\\\)를 구하면:\\\\\\\\n\\\\\\\\n\\\\\\\\\\\\\\\\[\\\\\\\\nx = \\\\\\\\\\\\\\\\frac{8}{2} = 4\\\\\\\\n\\\\\\\\\\\\\\\\]\\\\\\\\n\\\\\\\\n검증:\\\\\\\\n- \\\\\\\\\\\\\\\\(4^4 = 256\\\\\\\\\\\\\\\\)이고 \\\\\\\\\\\\\\\\(2^8 = 256\\\\\\\\\\\\\\\\)이므로 해가 맞음을 확인할 수 있습니다.\\\\\\\\n- 로그를 사용한 검증도 결과를 확인합니다.\\\\\\\\n\\\\\\\\n따라서, \\\\\\\\\\\\\\\\(x\\\\\\\\\\\\\\\\)의 값은 \\\\\\\\\\\\\\\\(\\\\\\\\\\\\\\\\boxed{4}\\\\\\\\\\\\\\\\)입니다.\\n</solution>\"]}]}]}]}]}]}]}]}]}]},{t:\"p\",a:{},c:[\"R1 모델의 성능을 확인하기 위해 HRM8K의 서브셋인 GSM8K, MATH, Omni-MATH에서 평가했습니다. (KSM 서브셋은 데이터셋에 약간의 업데이트가 이뤄질 예정으로, 추후 평가하여 업데이트하겠습니다.) 평가에 사용된 하이퍼파라미터는 다음과 같습니다.\"]},{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"pre\",a:{},c:[]}]}]}]}]}]}]}]}]},{t:\"p\",a:{},c:[\"You can see the models outperforming models of similar size and even those that are way bigger. It only struggles in GSM8K, which is similar to findings from \",{t:\"a\",a:{href:\"https://arxiv.org/abs/2412.21187\"},c:[\"Chen et al., 2025\"]},\", where these o1-like LLMs trained to generate longer sequences tend to overthink in easier questions.\"]},{t:\"p\",a:{},c:[\"평가 결과, Ko-R1 모델은 동사이즈 뿐만 아니라 훨씬 큰 사이즈의 모델과도 비슷한 수준의 성능을 보여줬습니다. Ko-R1 모델은 GSM8K에서만 뒤쳐지는 성능을 보여주는데, 이는 \",{t:\"a\",a:{href:\"https://arxiv.org/abs/2412.21187\"},c:[\"Chen et al., 2025\"]},\"의 finding과 마찬가지로 o1-like 모델들이 긴 시퀀스를 생성하기 위해 학습되었기 때문에 쉬운 문제에 대해서도 지나치게 생각하기 때문입니다.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/KAwbhmmEGZxMzzQCPLo1y3i2hfk.png?width=3844&height=832\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"Ko-R1-1.5B 모델과 평가 결과 파일은 다음의 링크에서 확인하실 수 있으며, 더 큰 사이즈의 모델도 추후 공개 예정입니다!\"]},{t:\"p\",a:{},c:[\"Enjoy the model here -> \",{t:\"a\",a:{href:\"https://huggingface.co/OLAIR/ko-r1-1.5b-preview\"},c:[\"[Link to Model]\"]}]}],toc:[],images:[\"https://framerusercontent.com/images/Nmuud8urGZAVmo3drOi2zMvhY.png?width=1973&height=992\",\"https://framerusercontent.com/images/KAwbhmmEGZxMzzQCPLo1y3i2hfk.png?width=3844&height=832\"],readMins:9},{id:12,page:\"Journal 13\",slug:\"olaf2-test-time-scaling\",kind:\"research\",topic:\"Reasoning\",title:\"Test-Time Scaling with OLAF2\",originalTitle:\"Test-Time Scaling with OLAF2\",summary:\"Reasoning at inference time, examined through OLAF2 experiments.\",language:\"en\",date:\"Jan 16, 2025\",dateISO:\"2025-01-16\",source:\"https://onelineai.com/en/blog-research/test-time-scaling\",path:\"/research/olaf2-test-time-scaling\",nodes:[{t:\"p\",a:{},c:[\"Test-Time Scaling has emerged as an exciting area of research, with recent models like \",{t:\"strong\",a:{},c:[\"QWQ\"]},\", \",{t:\"strong\",a:{},c:[\"O1\"]},\", and \",{t:\"strong\",a:{},c:[\"Deepseek-R1 \"]},\"demonstrating surprising reasoning capabilities. Test-Time Scaling can be approached through various methods, including \",{t:\"strong\",a:{},c:[\"Best-of-N\"]},\", \",{t:\"strong\",a:{},c:[\"Monte Carlo Tree Search\"]},\", and \",{t:\"strong\",a:{},c:[\"Reflective Tuning\"]},\". In this post, we share our preliminary findings from applying test-time scaling techniques to \",{t:\"strong\",a:{},c:[\"OLAF2-14B\"]},\", our flagship model.\"]},{t:\"h3\",a:{id:\"section-1\"},c:[\"Experimental Setup\"]},{t:\"p\",a:{},c:[\"The figure below showcases Deepseek’s results on test-time scaling, where the \",{t:\"strong\",a:{},c:[\"average number of thought tokens\"]},\" is plotted on the X-axis.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/ePjGmZJ3EJl06pRXoqhYMS2YV4.png?width=960&height=416\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"In contrast, our experiments involve applying multiple scaling methods simultaneously, which complicates the calculation of token counts. Some tokens have inherently higher value than others. To address this, we use \",{t:\"strong\",a:{},c:[\"FLOPs (Floating Point Operations)\"]},\" as a more consistent metric. FLOPs are computed following the approach outlined in the \",{t:\"em\",a:{},c:[\"Scaling Laws for Neural Language Models\"]},\" paper. Specifically, a single forward pass is approximated as:\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/fZlwlV3YYbKwFlcv4OMNGEYynM.png?width=960&height=138\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"Here:\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"code\",a:{},c:[\"n_{layer}\"]},\": Number of layers\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"code\",a:{},c:[\"d_{model}\"]},\": Dimension of the residual stream\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"code\",a:{},c:[\"n_{ctx}\"]},\": Number of tokens in the input context\"]}]}]},{t:\"p\",a:{},c:[\"We benchmark our methods on the \",{t:\"strong\",a:{},c:[\"GSM8K\"]},\" and the \",{t:\"strong\",a:{},c:[\"Omni Math\"]},\" subset of \",{t:\"strong\",a:{},c:[\"HRM8K\"]},\". While it would have been ideal to include more subsets and benchmarks, due to compute constraints we focus on these two. This selection is motivated by two key reasons:\"]},{t:\"ol\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Diversity in Difficulty\"]},\": GSM8K represents relatively easy, school-level math word problems, while Omni Math includes olympiad-level, highly challenging problems.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Simplified Evaluation\"]},\": Both subsets have been pre-filtered to include only questions with digit-based answers, simplifying the evaluation process.\"]}]}]},{t:\"p\",a:{},c:[\"For more details about the benchmark, please refer to our paper.\"]},{t:\"h3\",a:{id:\"section-2\"},c:[\"Evaluation Results\"]},{t:\"p\",a:{},c:[\"To our surprise, increasing test-time compute significantly enhances the performance of OLAF2-14B. The efficiency of this scaling, however, depends heavily on \",{t:\"em\",a:{},c:[\"how\"]},\" the compute is utilized, as some methods are far more effective than others. When scaled to the extreme, OLAF2-14B surpasses GPT-4o on both benchmarks.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/VBMDWMtThzSGJuAxvLR5Z8gOSTk.png?width=960&height=446\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/CXRZyzTvUxamn5HJni3I3kf4I.png?width=960&height=464\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]}],toc:[{id:\"section-1\",text:\"Experimental Setup\"},{id:\"section-2\",text:\"Evaluation Results\"}],images:[\"https://framerusercontent.com/images/ePjGmZJ3EJl06pRXoqhYMS2YV4.png?width=960&height=416\",\"https://framerusercontent.com/images/fZlwlV3YYbKwFlcv4OMNGEYynM.png?width=960&height=138\",\"https://framerusercontent.com/images/VBMDWMtThzSGJuAxvLR5Z8gOSTk.png?width=960&height=446\",\"https://framerusercontent.com/images/CXRZyzTvUxamn5HJni3I3kf4I.png?width=960&height=464\"],readMins:2},{id:13,page:\"Journal 14\",slug:\"olaf2-test-time-scaling-ko\",kind:\"research\",topic:\"Reasoning\",title:\"OLAF2 Test-Time Scaling (한글버전)\",originalTitle:\"OLAF2 Test-Time Scaling (한글버전)\",summary:\"OLAF2 실험으로 살펴보는 추론 시간 확장\",language:\"ko\",date:\"Jan 16, 2025\",dateISO:\"2025-01-16\",source:\"https://onelineai.com/en/blog-research/olaf2-test-time-scaling\",path:\"/research/olaf2-test-time-scaling-ko\",nodes:[{t:\"p\",a:{},c:[\"O1, QwQ, Deepseek-R1 같은 모델들이 뛰어난 추론 능력을 선보이며 Test-Time Scaling 방법론이 최근 흥미로운 연구 분야로 주목받고 있습니다. Test-Time Scaling은 \",{t:\"strong\",a:{},c:[\"Best-of-N\"]},\", \",{t:\"strong\",a:{},c:[\"Monte Carlo Tree Search\"]},\", \",{t:\"strong\",a:{},c:[\"Reflective Tuning\"]},\" 등 다양한 접근법을 통해 구현할 수 있습니다. 이번 포스트에서는 당사의 주력 모델인 OLAF2-14B에 Test-Time Scaling 기법을 적용해 얻은 초기 연구 결과를 공유하고자 합니다.\"]},{t:\"h3\",a:{id:\"section-1\"},c:[\"Experimental Setup\"]},{t:\"p\",a:{},c:[\"다음의 그래프는 X 축에 \",{t:\"strong\",a:{},c:[\"평균 thought token의 수\"]},\"를 기록한 Deepseek의 test-time scaling 결과입니다.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/ePjGmZJ3EJl06pRXoqhYMS2YV4.png?width=960&height=416\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"Deepseek의 test-time scaling 실험은 단일 방법론을 사용한 반면, 우리는 여러 스케일링 방식을 동시에 적용하는 방법을 사용하였습니다. 이 과정에서 토큰 수를 계산하는 데 어려움-일부 토큰이 다른 토큰보다 더 높은 비용이 발생-이 있습니다. 이를 해결하기 위해 토큰 수 대신 보다 일관된 지표인 \",{t:\"strong\",a:{},c:[\"FLOPs (Floating Point Operations)\"]},\"를 활용하였습니다. FLOPs의 계산은 \",{t:\"em\",a:{},c:[\"Scaling Laws for Neural Language Models\"]},\" 논문에서 제시된 접근법을 따랐으며, 단일 포워드 패스에 대한 FLOPs는 다음과 같이 계산할 수 있습니다:\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/fZlwlV3YYbKwFlcv4OMNGEYynM.png?width=960&height=138\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"위 수식의 각 변수들은 다음과 같은 의미를 가집니다:\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"code\",a:{},c:[\"n_{layer}\"]},\": 모델 레이어의 수\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"code\",a:{},c:[\"d_{model}\"]},\": residual stream의 차원(디멘션) 수\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"code\",a:{},c:[\"n_{ctx}\"]},\": input context의 토큰 수\"]}]}]},{t:\"p\",a:{},c:[\"당사에서 제안한 Test-Time Scaling 방법을 \",{t:\"strong\",a:{},c:[\"HRM8K\"]},\"의 서브셋인 \",{t:\"strong\",a:{},c:[\"GSM8K\"]},\"와 \",{t:\"strong\",a:{},c:[\"Omni-MATH\"]},\"를 통해 평가하였습니다. 두 서브셋은 다음과 같은 이유로 선택하였습니다. (향후 더 다양한 벤치마크에서의 결과를 발표 할 예정입니다.) :\"]},{t:\"ol\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"난이도 다양성\"]},\": GSM8K는 비교적 쉽고, 초중등 수준의 수학 문제를 다루는 반면, Omni-MATH는 올림피아드 수준의 매우 어려운 문제를 다룹니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"간편한 평가\"]},\": 두 서브셋은 모두 정답이 실수인 문제들로만 구성되어 있기 때문에 간편하게 평가를 진행할 수 있습니다.\"]}]}]},{t:\"p\",a:{},c:[\"벤치마크에 대한 세부사항이 궁금하다면, \",{t:\"a\",a:{href:\"https://arxiv.org/abs/2501.02448\"},c:[{t:\"strong\",a:{},c:[\"HRM8K\"]}]},\" 논문을 참고하길 바랍니다.\"]},{t:\"h3\",a:{id:\"section-2\"},c:[\"Evaluation Results\"]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"GSM8K와 Omni-MATH 벤치마크에서의 평가 결과는 놀랍게도 테스트 시점에서 계산 자원을 대폭 늘리는 것이 OLAF2-14B의 성능 향상에 크게 도움이 된다는 것을 보여줍니다.\"]},\" 그리고 이러한 성능 향상의 효율성은 계산 자원을 활용하는 방식에 크게 좌우됩니다. 이를 \",{t:\"strong\",a:{},c:[\"실험 세팅의 극한까지 확장했을 때, OLAF2-14B는 두 가지 평가 지표에서 GPT-4o를 능가하는 결과를 보여줍니다.\"]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/VBMDWMtThzSGJuAxvLR5Z8gOSTk.png?width=960&height=446\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/CXRZyzTvUxamn5HJni3I3kf4I.png?width=960&height=464\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]}],toc:[{id:\"section-1\",text:\"Experimental Setup\"},{id:\"section-2\",text:\"Evaluation Results\"}],images:[\"https://framerusercontent.com/images/ePjGmZJ3EJl06pRXoqhYMS2YV4.png?width=960&height=416\",\"https://framerusercontent.com/images/fZlwlV3YYbKwFlcv4OMNGEYynM.png?width=960&height=138\",\"https://framerusercontent.com/images/VBMDWMtThzSGJuAxvLR5Z8gOSTk.png?width=960&height=446\",\"https://framerusercontent.com/images/CXRZyzTvUxamn5HJni3I3kf4I.png?width=960&height=464\"],readMins:2},{id:14,page:\"Journal 15\",slug:\"korean-reasoning-benchmarks\",kind:\"research\",topic:\"Evaluation\",title:\"Korean Reasoning Benchmarks: HRM8K & HRMCR\",originalTitle:\"Korean Reasoning Benchmarks: HRM8K & HRMCR\",summary:\"The design and evaluation of Korean reasoning benchmarks.\",language:\"en\",date:\"Jan 16, 2025\",dateISO:\"2025-01-16\",source:\"https://onelineai.com/en/blog-research/korean-reasoning-benchmarks\",path:\"/research/korean-reasoning-benchmarks\",nodes:[{t:\"p\",a:{},c:[\"We are sharing two new Korean reasoning benchmarks: HRM8K (\",{t:\"strong\",a:{},c:[\"H\"]},\"AE-\",{t:\"strong\",a:{},c:[\"R\"]},\"AE \",{t:\"strong\",a:{},c:[\"M\"]},\"ath \",{t:\"strong\",a:{},c:[\"8K\"]},\") and HRMCR (\",{t:\"strong\",a:{},c:[\"H\"]},\"AE-\",{t:\"strong\",a:{},c:[\"R\"]},\"AE \",{t:\"strong\",a:{},c:[\"M\"]},\"ulti-Step \",{t:\"strong\",a:{},c:[\"C\"]},\"ommonsense \",{t:\"strong\",a:{},c:[\"R\"]},\"easoning). These benchmarks are carefully designed to assess the Korean mathematical reasoning skills and commonsense reasoning capabilities of Large Language Models (LLMs). In this post, we first highlight the importance of evaluating reasoning capability in Korean LLMs, explain each benchmark in details, and share insights from their evaluation results.\"]},{t:\"p\",a:{},c:[\"The paper and benchmark datasets are publicly available below:\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"📝\\xa0Paper (HRM8K): \",{t:\"a\",a:{href:\"https://arxiv.org/abs/2501.02448\"},c:[\"https://arxiv.org/abs/2501.02448\"]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"📝\\xa0Paper (HRMCR): \",{t:\"a\",a:{href:\"https://arxiv.org/abs/2501.05712\"},c:[\"https://arxiv.org/abs/2501.05712\"]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"🤗\\xa0Dataset (HRM8K): \",{t:\"a\",a:{href:\"https://huggingface.co/datasets/HAERAE-HUB/HRM8K\"},c:[\"https://huggingface.co/datasets/HAERAE-HUB/HRM8K\"]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"🤗\\xa0Dataset (HRMCR): \",{t:\"a\",a:{href:\"https://huggingface.co/datasets/HAERAE-HUB/HRMCR\"},c:[\"https://huggingface.co/datasets/HAERAE-HUB/HRMCR\"]}]}]}]},{t:\"h2\",a:{id:\"section-1\"},c:[{t:\"strong\",a:{},c:[\"The Lack of Korean Reasoning Evaluation\"]}]},{t:\"p\",a:{},c:[\"The recent advancements in powerful Large Language Models (LLMs), such as OpenAI's o1 and Anthropic’s Claude-3.5, are leading a significant evolution in the field. These developments are not confined to the English but are also fostering the development of multilingual LLMs, including Korean. Consequently, the need for benchmarks that can systematically and fairly evaluate the performance of LLMs across diverse linguistic environments, including Korean, has become increasingly critical.\"]},{t:\"p\",a:{},c:[\"To address this, various benchmarks have been specifically developed to assess LLMs' Korean language capabilities. The following table describes highly-used benchmarks to evaluate LLMs’ Korean capabilities with 3 primary categories: \",{t:\"code\",a:{},c:[\"Knowledge, Fluency, and Reasoning.\"]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/S3Zoi4mqDZYNlrKU3fyv0jmm0TU.png?width=960&height=384\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Knowledge\"]},\": Benchmarks designed to evaluate the performance of language models in comprehending and processing the Korean language and cultural knowledge. These benchmarks feature question sets covering various aspects of Korean knowledge and culture, such as society, history, law, and more: \",{t:\"em\",a:{},c:[\"CLIcK\"]},\", \",{t:\"em\",a:{},c:[\"HAE-RAE Bench\"]},\", \",{t:\"em\",a:{},c:[\"KMMLU\"]},\", \",{t:\"em\",a:{},c:[\"Ko-H5\"]},\", etc.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Fluency\"]},\": Benchmarks designed to assess a language model's Korean instruction-following ability and fluency. These benchmarks often employ the LLM-as-a-Judge approach, where large language models act as evaluators to measure the quality and accuracy of responses: \",{t:\"em\",a:{},c:[\"KoMT-Bench\"]},\", \",{t:\"em\",a:{},c:[\"LogicKor\"]},\", etc.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Reasoning\"]},\": Benchmarks used to evaluate a language model's ability to reasoning in Korean. These tasks range from basic Korean knowledge to complex scenarios that require specialized knowledge and logical inference. However, these benchmarks are outdated or limited by the domain, making it challenging to evaluate the model's complex reasoning capabilities: \",{t:\"em\",a:{},c:[\"KoBEST\"]},\", \",{t:\"em\",a:{},c:[\"KMMLU\"]},\", \",{t:\"em\",a:{},c:[\"Ko-H5\"]},\", \",{t:\"em\",a:{},c:[\"KoCommonGen v2\"]},\", etc.\"]}]}]},{t:\"p\",a:{},c:[\"These benchmarks primarily focus on tasks like language understanding, general knowledge, and commonsense reasoning, making it challenging to assess the reasoning capability—one of the critical capabilities of language models. Furthermore, most reasoning benchmarks primarily focus on commonsense reasoning, and their limited accessibility remains a significant challenge for evaluation.\"]},{t:\"p\",a:{},c:[\"To address these challenges, we introduce \",{t:\"strong\",a:{},c:[\"HRM8K\"]},\" and \",{t:\"strong\",a:{},c:[\"HRMCR\"]},\", the fully publicly available Korean reasoning benchmarks which focus on mathematical reasoning and cultural commonsense reasoning, respectively.\"]},{t:\"h2\",a:{id:\"section-2\"},c:[{t:\"strong\",a:{},c:[\"HRM8K: Korean Mathematical Reasoning Benchmark\"]}]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"HRM8K\"]},\" is first publicly available benchmark for mathematical reasoning in Korean. This benchmark comprises 8,011 instances sourced through a combination of translations from established English benchmarks (GSM8K, MATH, Omni-MATH, and MMMLU) and original problems curated from Korean math exams and competitions.\"]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"Benchmark Overview\"]}]},{t:\"p\",a:{},c:[\"The\\xa0\",{t:\"strong\",a:{},c:[\"HRM8K\"]},\"\\xa0benchmark consists of two subsets, and each subset is available in both Korean and English. To create a bilingual (English-Korean) dataset, we translated each instance in both subsets using GPT-4o, followed by human review of the translated samples to ensure quality:\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Korean School Math\"]},\"\\xa0(\",{t:\"strong\",a:{},c:[\"KSM\"]},\"): This subset comprises 1,428 challenging mathematical problems from Korean sources. We collect only from Olympiad or competition-level exams, regardless of the target age group. Consequently, even problems from younger curricula require a certain level of reasoning ability to solve. We manually capture the problems collected from the following sources as screenshots, process them through OCR using the GPT-4o API, and validate.\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"em\",a:{},c:[\"Sources\"]},\": KMO (한국수학올림피아드), KJMO (한국주니어수학올림피아드), CSAT (대학수학능력시험), KMS (한국대학수학경시대회), and TQ (교원임용경쟁시험).\"]}]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Prior Sets\"]},\": This subset comprises 6,583 problems from existing English mathematics benchmarks. We retain only instances with numeric answers for the MATH and Omni-MATH datasets, excluding those with text, equations, or proofs as final answers. In addition, we select only three math-related subsets, including\\xa0\",{t:\"code\",a:{},c:[\"abstract_algebra\"]},\",\\xa0\",{t:\"code\",a:{},c:[\"college_mathematics\"]},\", and\\xa0\",{t:\"code\",a:{},c:[\"high_school_mathematics\"]},\"\\xa0from MMMLU datasets. The sources from which data was collected are as follows:\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"em\",a:{},c:[\"Sources\"]},\": \",{t:\"a\",a:{href:\"https://huggingface.co/datasets/openai/gsm8k\"},c:[\"GSM8K\"]},\", \",{t:\"a\",a:{href:\"https://huggingface.co/datasets/hendrycks/competition_math\"},c:[\"MATH\"]},\", \",{t:\"a\",a:{href:\"https://huggingface.co/datasets/KbsdJames/Omni-MATH\"},c:[\"Omni-MATH\"]},\", and \",{t:\"a\",a:{href:\"https://huggingface.co/datasets/openai/MMMLU\"},c:[\"MMMLU\"]}]}]}]}]}]},{t:\"h3\",a:{id:\"section-3\"},c:[\"Experimental Setup\"]},{t:\"p\",a:{},c:[\"We evaluate models across three cross-lingual setups (input language - to - reasoning language) to analyze the performance variations based on changes in the input and reasoning language, the evaluated settings are: Korean-to-Korean (K2K), Korean-to-English(K2E), English-to-English(E2E). We exclude the English-to-Korean (E2K) scenario because models typically fail to maintain a Korean reasoning when the input is given in English.\"]},{t:\"p\",a:{},c:[\"We experiment with six multilingual language models with decent Korean performance: three Qwen2.5 Instruct models (1.5B, 7B, and 72B parameters) and three Llama3.1/2 Instruct models (1B, 8B, and 70B parameters). We set the sampling parameters to \",{t:\"code\",a:{},c:[\"temperature=0.7\"]},\", \",{t:\"code\",a:{},c:[\"top_p=0.95\"]},\" .\"]},{t:\"h3\",a:{id:\"section-4\"},c:[\"Results & Findings\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/xgTxxJUEemISDHw5GbUP4Gfbpis.png?width=1920&height=560\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"The above table shows the performance of Qwen2.5 and Llama3.1/2 models on the HRM8K benchmark depending on the input and reasoning language. We highlight three findings on these results:\"]},{t:\"ol\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Effect of input language\"]},\": Switching from Korean input (K2E) to an entirely English setup (E2E) yields an average improvement of 11%. In particular, Qwen2.5-7B and Llama-3.1-8B drop by 10% and 13%, respectively, when forced to process Korean input. This underscores the significance of input language in model performance.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Effect of reasoning language\"]},\": In contrast, comparing K2K to K2E shows an average difference of only 1%, suggesting that the language of the reasoning process has a relatively small impact once the model has already ingested Korean input. Simply allowing the model to produce its chain-of-thought in English does not fully recover performance lost from reading a Korean prompt.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Multilingual reasoning gap\"]},\": Comparing problem-solving in a Korean (K2K) to an English (E2E), we observed an approximate 11% improvement in average performance in E2E. This finding highlights the multilingual reasoning gap, discussed in various studies, as evident in the HRM8K dataset.\"]}]}]},{t:\"p\",a:{},c:[\"To recap, we identify a clear multilingual reasoning gap between English and Korean, and model's capability to comprehend the problem seems as a critical factor.\"]},{t:\"h2\",a:{id:\"section-5\"},c:[{t:\"strong\",a:{},c:[\"HRMCR: Korean Cultural Reasoning Benchmark\"]}]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"HRMCR\"]},\" is benchmark consists of cultural multi-step reasoning questions automatically generated using templates and algorithms. The questions in\\xa0HRMCR\\xa0require LLMs to recall diverse aspects of Korean culture and perform multiple reasoning steps to solve them.\"]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"Benchmark Overview\"]}]},{t:\"p\",a:{},c:[\"The\\xa0\",{t:\"strong\",a:{},c:[\"HRMCR\"]},\"\\xa0comprises two subsets, each containing 50 systematically designed questions that require multi-step reasoning. The specific details and example of each subset are illustrated as follows:\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Date\"]},\": Questions in this subset are composed of two sentences and require recalling knowledge about Korean holidays and data expressions. The model must perform simple arithmetic and convert between lunar and solar years using this knowledge. Each question requires a four-step solution.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Zodiac\"]},\": This subset contains longer questions, each spanning 10 to 12 lines. To solve these, the model must recall concepts such as the Korean age and various age expressions used in Korean conversations, understand honorifics, and make logical inferences based on the provided premises. Additionally, it must perform arithmetic to deduce the zodiac sign from the age. The gold response consists of six steps, making this subset more challenging than the\\xa0\",{t:\"em\",a:{},c:[\"Date\"]},\"\\xa0subset.\"]}]}]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/dYyz2TNFkmzxMGMkUz9N10ZoG8.png?width=1920&height=608\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:['The above figure shows generated example questions (left) alongside their automatically generated solutions (right). The top panel represents the \"Date\" subset, while the bottom corresponds to the \"Zodiac\" subset. Questions are translated into Korean to enhance accessibility.']},{t:\"img\",a:{src:\"https://framerusercontent.com/images/Kyr8naGoNAYtH9yQNCF7Dcan04.png?width=960&height=394\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"All questions of each subset are created by a dedicated algorithm with template. Each algorithm natively includes a solution generator that solves the question step-by-step as it is generated and provides a gold solution. The unique characteristics of HRMCR enable quick and easy generation of new data compared to other private and public datasets.\"]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"Experimental Setup\"]}]},{t:\"p\",a:{},c:[\"We evaluate a total of 20 LLMs, including both proprietary (o1, GPT-4o, Claude-3.5) and open-source (Qwen2.5, Llama3, EXAONE-3.5, DeepSeek-V3) models. For evaluation process, we employ GPT-4o as an LLM-as-a-Judge. The judge reviews each question alongside the model-generated response and the gold step-by-step solution. It first provides a brief comparison with the gold solution and then assesses whether the model’s response is correct. In cases of incorrect responses, the judge pinpoints the specific step where the error occurred. All evaluations were conducted using greedy decoding.\"]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"Results & Findings\"]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/vtmi9IjCFPYkUki9mHPv0m99mU.png?width=1920&height=432\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"The above table shows the evaluation results on HRMCR. We only display the performance of top-performing models per model family. We can derive three key observations from the evaluation results:\"]},{t:\"ol\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"HRMCR is highly challenging\"]},\": The leading models such as GPT-4o, DeepSeek-V3, and Claude-3.5-Sonnet all score under 30%. This is particularly noteworthy given that the benchmark is built on fixed, deterministic rules rather than specialized domain knowledge.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"The effectiveness of inference-time scaling\"]},\": O1 achieves an average score of 45. This suggests that inference-time scaling can generalize effectively to previously unseen domains.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Computational resources\"]},\": EXAONE3.5-32B, despite its size, shows near-zero performance on the benchmark. On the other hand, as shown in the following figure(X-axis: ExaFLOPs(10^18) training compute scale, Y-axis: HRMCR performance), Qwen-2.5-14B, despite a much smaller model size, demonstrates superior performance compared to EXAONE-3.5-32B due to its higher training compute. This indicates that solving HRMCR requires not just model scale but also advanced training strategies and sufficient computational resources.\"]}]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/zwexiw1HV09Jv4TBxegTm2enJsU.png?width=1920&height=464\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-6\"},c:[{t:\"strong\",a:{},c:[\"Conclusion\"]}]},{t:\"p\",a:{},c:[\"We introduce HRM8K and HRMCR, designed to evaluate mathematical reasoning and cultural commonsense reasoning, respectively, to address the lack of reasoning benchmarks in Korean. We evaluate various models agains theses benchmarks, and analyzed their reasoning capabilities in the Korean language and multilingual setup. Along the way, we uncover insights such as the multilingual reasoning gap and the role of computational resources in model performance.\"]},{t:\"p\",a:{},c:[\"Our findings underscore the significant potential for further research into the Korean reasoning capabilities of LLMs. We hope that introduction of HRM8K and HRMCR contributes to Korean LLM community and provides valuable resources for future researches in this domain. If you have any questions about our benchmarks, feel free to contact us at \",{t:\"code\",a:{},c:[\"spthsrbwls123@yonsei.ac.kr\"]},\".\"]}],toc:[{id:\"section-1\",text:\"The Lack of Korean Reasoning Evaluation\"},{id:\"section-2\",text:\"HRM8K: Korean Mathematical Reasoning Benchmark\"},{id:\"section-3\",text:\"Experimental Setup\"},{id:\"section-4\",text:\"Results & Findings\"},{id:\"section-5\",text:\"HRMCR: Korean Cultural Reasoning Benchmark\"},{id:\"section-6\",text:\"Conclusion\"}],images:[\"https://framerusercontent.com/images/S3Zoi4mqDZYNlrKU3fyv0jmm0TU.png?width=960&height=384\",\"https://framerusercontent.com/images/xgTxxJUEemISDHw5GbUP4Gfbpis.png?width=1920&height=560\",\"https://framerusercontent.com/images/dYyz2TNFkmzxMGMkUz9N10ZoG8.png?width=1920&height=608\",\"https://framerusercontent.com/images/Kyr8naGoNAYtH9yQNCF7Dcan04.png?width=960&height=394\",\"https://framerusercontent.com/images/vtmi9IjCFPYkUki9mHPv0m99mU.png?width=1920&height=432\",\"https://framerusercontent.com/images/zwexiw1HV09Jv4TBxegTm2enJsU.png?width=1920&height=464\"],readMins:8},{id:15,page:\"Journal 16\",slug:\"korean-reasoning-benchmarks-ko\",kind:\"research\",topic:\"Evaluation\",title:\"한국어 추론 벤치마크: HRM8K & HRMCR\",originalTitle:\"한국어 추론 벤치마크: HRM8K & HRMCR\",summary:\"한국어 추론을 평가하기 위한 벤치마크 설계와 분석\",language:\"ko\",date:\"Jan 16, 2025\",dateISO:\"2025-01-16\",source:\"https://onelineai.com/en/blog-research/%ED%95%9C%EA%B5%AD%EC%96%B4-%EC%B6%94%EB%A1%A0-%EB%B2%A4%EC%B9%98%EB%A7%88%ED%81%AC-hrm8k-hrmcr\",path:\"/research/korean-reasoning-benchmarks-ko\",nodes:[{t:\"p\",a:{},c:[\"새로운 한국어 추론 벤치마크 HRM8K(\",{t:\"strong\",a:{},c:[\"H\"]},\"AE-\",{t:\"strong\",a:{},c:[\"R\"]},\"AE \",{t:\"strong\",a:{},c:[\"M\"]},\"ath \",{t:\"strong\",a:{},c:[\"8K\"]},\")와 HRMCR(\",{t:\"strong\",a:{},c:[\"H\"]},\"AE-\",{t:\"strong\",a:{},c:[\"R\"]},\"AE \",{t:\"strong\",a:{},c:[\"M\"]},\"ulti-Step \",{t:\"strong\",a:{},c:[\"C\"]},\"ommonsense \",{t:\"strong\",a:{},c:[\"R\"]},\"easoning)을 소개합니다! 각 벤치마크는 대규모 언어 모델(LLM)의 한국어 수학 추론 (mathematical reasoning) 능력과 상식 추론 (commonsense reasoning) 능력을 평가하기 위해 설계되었습니다. 이 포스트에서는 한국어 기반 LLM의 추론 능력을 평가하는 것의 중요성과 각 벤치마크에 대한 상세한 설명과 평가 결과에서 얻은 통찰을 이야기하고자 합니다.\"]},{t:\"p\",a:{},c:[\"\\b논문과 벤치마크 데이터셋은 다음의 링크에서 확인하실 수 있습니다:\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"📝\\xa0HRM8K 논문: \",{t:\"a\",a:{href:\"https://arxiv.org/abs/2501.02448\"},c:[\"https://arxiv.org/abs/2501.02448\"]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"📝\\xa0HRMCR 논문: \",{t:\"a\",a:{href:\"https://arxiv.org/abs/2501.05712\"},c:[\"https://arxiv.org/abs/2501.05712\"]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"🤗\\xa0HRM8K 데이텃셋: \",{t:\"a\",a:{href:\"https://huggingface.co/datasets/HAERAE-HUB/HRM8K\"},c:[\"https://huggingface.co/datasets/HAERAE-HUB/HRM8K\"]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"🤗\\xa0HRMCR 데이터셋: \",{t:\"a\",a:{href:\"https://huggingface.co/datasets/HAERAE-HUB/HRMCR\"},c:[\"https://huggingface.co/datasets/HAERAE-HUB/HRMCR\"]}]}]}]},{t:\"h2\",a:{id:\"section-1\"},c:[{t:\"strong\",a:{},c:[\"부족한 한국어 추론 평가\"]}]},{t:\"p\",a:{},c:[\"최근 OpenAI의 o1, Anthropic의 Claude-3.5와 같은 강력한 LLM의 발전은 이 분야에 큰 혁신을 가져오고 있습니다. 이러한 발전은 영어에만 국한되지 않고, 한국어를 포함한 다국어 언어 모델의 개발에도 기여하고 있습니다. 이에 따라 한국어를 포함한 다양한 언어 환경에서 LLM의 성능을 체계적이고 공정하게 평가할 수 있는 벤치마크의 필요성이 그 어느 때보다 중요해지고 있습니다.\"]},{t:\"p\",a:{},c:[\"이를 해결하기 위해 LLM의 한국어 능력을 평가하기 위한 다양한 벤치마크가 개발되었습니다. 아래 표에서는 LLM의 한국어 능력을 평가하는 데 자주 사용되는 벤치마크를 보여주고 있고, 이를 크게 지식, 유창성, 그리고 추론의 세 가지 주요 범주로 나누어 설명하고자 합니다.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/S3Zoi4mqDZYNlrKU3fyv0jmm0TU.png?width=960&height=384\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"지식\"]},\": 언어 모델이 한국어와 한국 문화에 대한 이해 및 처리 능력을 평가하기 위해 설계된 벤치마크. 이 벤치마크는 사회, 역사, 법률 등 한국의 지식과 문화를 다양한 측면에서 다루는 질문 세트로 구성되어 있음: \",{t:\"em\",a:{},c:[\"CLIcK\"]},\", \",{t:\"em\",a:{},c:[\"HAE-RAE Bench\"]},\", \",{t:\"em\",a:{},c:[\"KMMLU\"]},\", \",{t:\"em\",a:{},c:[\"Ko-H5\"]},\" 등\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"유창성\"]},': 언어 모델의 한국어 지시 수행 능력과 유창성을 평가하기 위해 설계된 벤치마크들. 이러한 벤치마크는 주로 언어 모델이 평가자로서 응답의 품질과 정확성을 판단하는 \"LLM-as-a-Judge\" 방식을 사용함: ',{t:\"em\",a:{},c:[\"KoMT-Bench\"]},\", \",{t:\"em\",a:{},c:[\"LogicKor\"]},\" 등\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"추론\"]},\": 언어 모델의 한국어 추론 능력을 평가하기 위해 사용되는 벤치마크. 기본적인 한국어 지식부터 전문적인 지식과 논리적 추론을 요하는 복잡한 시나리오까지 다양한 문제로 구성되어 있음. 그러나 이러한 벤치마크는 시대에 뒤떨어져 있거나 특정 분야에 국한되어 있어 모델의 복잡한 추론 능력을 제대로 평가하기 어렵다는 한계가 있음: \",{t:\"em\",a:{},c:[\"KoBEST\"]},\", \",{t:\"em\",a:{},c:[\"KMMLU\"]},\", \",{t:\"em\",a:{},c:[\"Ko-H5\"]},\", \",{t:\"em\",a:{},c:[\"KoCommonGen v2\"]},\" 등\"]}]}]},{t:\"p\",a:{},c:[\"이러한 벤치마크들은 주로 언어 이해, 일반 상식, 그리고 상식 추론과 같은 태스크에 초점을 맞추고 있어, 언어 모델의 핵심 능력 중 하나인 '추론 능력'을 제대로 평가하기 어렵게 만듭니다. 더불어, 대부분의 추론 벤치마크는 상식적인 추론에 집중되어 있으며, 접근성이 제한적이라는 점도 평가 과정에서 큰 걸림돌로 작용하고 있습니다.\"]},{t:\"p\",a:{},c:[\"이 문제를 해결하기 위해, 우리는 수학적 추론에 초점을 맞춘 \",{t:\"strong\",a:{},c:[\"HRM8K\"]},\"와 문화적 상식 추론을 다루는 \",{t:\"strong\",a:{},c:[\"HRMCR\"]},\"이라는 한국어 추론 벤치마크를 새롭게 선보입니다.\"]},{t:\"h2\",a:{id:\"section-2\"},c:[{t:\"strong\",a:{},c:[\"HRM8K: 한국어 수학 추론 벤치마크\"]}]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"HRM8K\"]},\"는 처음으로 공개된 한국어 수학 추론 능력 평가 벤치마크입니다. 이 벤치마크는 기존 영어 벤치마크(GSM8K, MATH, Omni-MATH, MMMLU)를 번역한 데이터와 한국 수학 시험 및 경시대회에서 수집된 문제 총 8,011개로 구성되어 있습니다.\"]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"벤치마크 개요\"]}]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"HRM8K\"]},\" 벤치마크는 두 개의 하위 집합으로 구성되어 있으며, 각각 한국어와 영어 데이터로 구성되어 있습니다. 영어-한국어 이중 언어 데이터셋을 만들기 위해, 두 하위 집합의 모든 항목을 GPT-4o로 번역한 뒤, 번역된 샘플의 품질을 보장하기 위해 사람이 직접 검토를 진행했습니다:\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Korean School Math\"]},\" (\",{t:\"strong\",a:{},c:[\"KSM\"]},\"): 한국의 수학 시험 및 경시대회에서 수집된 1,428개의 수학 문제로 구성되어 있습니다. 모든 문제는 대상 연령과 상관없이 수학 올림피아드나 대회 수준의 시험에서만 수집되었습니다. 따라서 초등 과정 문제라 하더라도 해결하려면 일정 수준의 논리적 사고 능력이 요구됩니다. 문제는 다음의 소스에서 수동으로 스크린샷을 캡처한 후, GPT-4o API를 이용한 OCR 처리와 검증 과정을 거쳐 확보하였습니다.\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"em\",a:{},c:[\"출처\"]},\": KMO (한국수학올림피아드), KJMO (한국주니어수학올림피아드), CSAT (대학수학능력시험), KMS (한국대학수학경시대회), and TQ (교원임용경쟁시험).\"]}]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Prior Sets\"]},\": 영어 수학 벤치마크에서 수집한 6,583개의 문제로 구성되어 있습니다. MATH와 Omni-MATH 데이터셋에서는 숫자 정답을 가진 문제만 남기고, 텍스트, 수식, 또는 증명이 최종 정답인 문제는 제외하였습니다. 또한, MMMLU 데이터셋에서는 수학 관련 하위 분야 중 \",{t:\"code\",a:{},c:[\"abstract_algebra\"]},\",\\xa0\",{t:\"code\",a:{},c:[\"college_mathematics\"]},\", \",{t:\"code\",a:{},c:[\"high_school_mathematics\"]},\" 세 가지로 한정하여 문제를 수집하였습니다.\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"em\",a:{},c:[\"출처\"]},\": \",{t:\"a\",a:{href:\"https://huggingface.co/datasets/openai/gsm8k\"},c:[\"GSM8K\"]},\", \",{t:\"a\",a:{href:\"https://huggingface.co/datasets/hendrycks/competition_math\"},c:[\"MATH\"]},\", \",{t:\"a\",a:{href:\"https://huggingface.co/datasets/KbsdJames/Omni-MATH\"},c:[\"Omni-MATH\"]},\", and \",{t:\"a\",a:{href:\"https://huggingface.co/datasets/openai/MMMLU\"},c:[\"MMMLU\"]}]}]}]}]}]},{t:\"h4\",a:{},c:[\"\\b\",{t:\"strong\",a:{},c:[\"실험 세팅\"]}]},{t:\"p\",a:{},c:[\"입력 언어와 추론 언어에 따른 모델 성능 변화를 분석하기 위해 세 가지 다국어 설정(입력 언어-to-추론 언어)에서 모델을 평가했습니다: Korean-to-Korean(K2K), Korean-to-English(K2E), English-to-English(E2E). English-to-Korean(E2K) 시나리오는 입력이 영어로 주어졌을 때 모델이 한국어 추론을 제대로 유지하지 못하는 경우가 많아 제외했습니다.\"]},{t:\"p\",a:{},c:[\"평가에는 한국어 처리 성능이 우수한 여섯 가지 다국어 언어 모델인 Qwen2.5 Instruct 모델(1.5B, 7B, 72B 파라미터) 세 가지와 Llama3.1/2 Instruct 모델(1B, 8B, 70B 파라미터) 세 가지를 사용하였습니다. 모델 답변 생성 시에는 \",{t:\"code\",a:{},c:[\"temperature=0.7\"]},\", \",{t:\"code\",a:{},c:[\"top_p=0.95\"]},\"로 하이퍼파라미터를 설정하였습니다.\"]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"실험 결과\"]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/xgTxxJUEemISDHw5GbUP4Gfbpis.png?width=1920&height=560\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"위의 표는 HRM8K 벤치마크에서 Qwen2.5와 Llama3.1/2 모델이 입력 언어와 추론 언어에 따라 보이는 성능 차이를 보여줍니다. 이 결과를 다음과 같이 세 가지의 주요 관찰점을 정리할 수 있습니다:\"]},{t:\"ol\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"입력 언어의 영향:\"]},\" 한국어 입력(K2E)에서 완전히 영어로 구성된 입력(E2E)으로 전환하면 평균적으로 성능이 11% 증가합니다. 특히, Qwen2.5-7B와 Llama-3.1-8B 모델은 한국어 입력을 처리할 때 각각 10%와 13% 성능이 감소하는 것으로 나타났습니다. 이는 입력 언어가 모델 성능에 중요한 영향을 미친다는 것을 보여줍니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"추론 언어의 영향:\"]},\" 반면, K2K와 K2E를 비교했을 때 평균 성능 차이는 1%에 불과해, 한국어 입력이 이미 주어진 상황에서는 추론 과정에서 사용하는 언어가 성능에 미치는 영향이 상대적으로 적음을 시사합니다. 즉, 모델이 영어로 사고 과정을 작성하도록 설정한다고 해도, 한국어 입력으로 인해 발생한 성능 저하를 완전히 회복하지는 못합니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"다국어 추론 격차:\"]},\" 한국어(K2K) 환경에서 문제를 해결하는 경우와 영어(E2E) 환경에서의 문제 해결 성능을 비교했을 때, 영어 환경에서는 평균적으로 약 11% 성능이 더 우수했습니다. 이는 다양한 연구에서 논의된 다국어 추론 격차가 HRM8K 데이터셋에서도 분명하게 나타나는 것을 보여줍니다.\"]}]}]},{t:\"p\",a:{},c:[\"결론적으로, 한국어와 영어 간의 명확한 다국어 추론 격차가 존재하며, 문제를 이해하는 모델의 역량이 매우 중요한 요소임을 확인할 수 있습니다.\"]},{t:\"h2\",a:{id:\"section-3\"},c:[{t:\"strong\",a:{},c:[\"HRMCR: 한국 문화 추론 벤치마크\"]}]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"HRMCR\"]},\" is benchmark consists of cultural multi-step reasoning questions automatically generated using templates and algorithms. The questions in\\xa0HRMCR\\xa0require LLMs to recall diverse aspects of Korean culture and perform multiple reasoning steps to solve them.\"]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"HRMCR\"]},\"은 템플릿과 알고리즘을 활용해 자동 생성된 문화적 다단계 추론 질문으로 구성된 벤치마크입니다. HRMCR의 질문들은 대규모 언어 모델(LLM)이 한국 문화의 다양한 측면을 기반으로 여러 단계의 추론 과정을 통해 문제를 해결하도록 합니다.\"]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"벤치마크 개요\"]}]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"HRMCR\"]},\"은 두 개의 서브셋으로 구성되어 있으며, 각 서브셋에는 다단계 추론이 필요한 50개의 질문이 포함되어 있습니다. 각 하위 집합의 세부 사항과 예시는 다음과 같습니다.\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"날짜(Date): \"]},\"두 문장의 질문으로 구성되며, 한국의 공휴일과 날짜 표현에 대한 지식을 활용하는 추론 문제들로 구성되어 있습니다. 해당 지식을 기반으로 간단한 산술 계산을 수행하고 음력과 양력을 변환해야 합니다. 각 질문은 총 네 단계의 풀이 과정을 요구합니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"띠(Zodiac): \"]},\"10~12줄에 이르는 긴 질문으로 구성되어 있습니다. 문제 해결을 위해 한국 나이와 한국어 대화에서 사용되는 다양한 나이 표현, 존칭을 이해하고 주어진 전제를 바탕으로 논리적 추론을 해야 합니다. 또한 나이로부터 띠를 추론하기 위해 산술 계산을 수행해야 합니다. 문제 해결에는 여섯 단계의 추론 과정이 필요하며, 이로 인해 날짜(Date) 서브셋보다 난이도가 높습니다.\"]}]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/dYyz2TNFkmzxMGMkUz9N10ZoG8.png?width=1920&height=608\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:['위의 그림은 각 서브셋의 예시 질문(왼쪽)과 솔루션(오른쪽)을 보여주며, 상단에는 \"Date\" 서브셋이, 하단에는 \"Zodiac\" 서브셋이 나타나고 있습니다. 해당 문제들은 모두 한국어로 구성되어 있으며, 이미지는 영어로 번역된 문제입니다.']},{t:\"img\",a:{src:\"https://framerusercontent.com/images/Kyr8naGoNAYtH9yQNCF7Dcan04.png?width=960&height=394\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"각 서브셋의 모든 질문은 템플릿을 활용한 전용 알고리즘으로 생성됩니다. 이 알고리즘은 질문을 단계별로 해결하면서 동시에 생성된 정답을 제공하는 솔루션 생성기를 기본적으로 포함하고 있습니다. 이러한 HRMCR의 독특한 특성 덕분에 다른 사설 또는 공공 데이터셋에 비해 새로운 데이터를 빠르고 손쉽게 생성하여 contamination으로부터 자유롭다는 장점을 가지고 있습니다.\"]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"실험 세팅\"]}]},{t:\"p\",a:{},c:[\"We evaluate a total of 20 LLMs, including both proprietary (o1, GPT-4o, Claude-3.5) and open-source (Qwen2.5, Llama3, EXAONE-3.5, DeepSeek-V3) models. For evaluation process, we employ GPT-4o as an LLM-as-a-Judge. The judge reviews each question alongside the model-generated response and the gold step-by-step solution. It first provides a brief comparison with the gold solution and then assesses whether the model’s response is correct. In cases of incorrect responses, the judge pinpoints the specific step where the error occurred. All evaluations were conducted using greedy decoding.\"]},{t:\"p\",a:{},c:[\"총 20개의 Proprietary 모델(o1, GPT-4o, Claude-3.5)과 오픈소스 모델(Qwen2.5, Llama3, EXAONE-3.5, DeepSeek-V3)을 평가하였습니다. 평가 과정에서는 GPT-4o를 'LLM-as-a-Judge'로 활용하여, 각 질문에 대해 모델이 생성한 응답과 정답이 포함된 단계별 솔루션을 함께 검토하도록 하였습니다. 이 과정을 통해 Judge 모델은 먼저 정답과 모델의 응답을 간략히 비교한 후, 응답이 정확한지 평가합니다. 만약 응답이 틀린 경우, 오류가 발생한 구체적인 단계를 지적합니다. 모든 평가는 greedy decoding 방식으로 진행되었습니다.\"]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"실험 결과\"]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/vtmi9IjCFPYkUki9mHPv0m99mU.png?width=1920&height=432\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"위 표는 HRMCR에 대한 평가 결과를 보여주며, 각 모델 계열에서 성능이 가장 뛰어난 표시되어 있습니다. 평가 결과에서 다음과 같은 세 가지 주요 관찰점을 도출할 수 있었습니다:\"]},{t:\"ol\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"HRMCR의 높은 난이도\"]},\": GPT-4o, DeepSeek-V3, Claude-3.5-Sonnet과 같은 고성능 모델조차도 30% 미만의 점수를 기록했습니다. 이는 HRMCR이 전문적인 도메인 지식이 아니라 고정된 결정론적 규칙을 기반으로 설계되었음에도 불구하고 상당한 난이도인 것을 보여줍니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"추론 단계 확장의 효과\"]},\": O1 모델은 평균 점수 45를 달성하며, inference-time scaling이 이전에 경험하지 못한 도메인에도 효과적으로 일반화될 수 있음을 시사합니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"계산 자원의 중요성\"]},\": EXAONE-3.5-32B는 모델 크기에도 불구하고 HRMCR에서 거의 0에 가까운 성능을 보였습니다. 반면, 다음 그림에서 볼 수 있듯이(X축: ExaFLOP(10^18) training compute, Y축: HRMCR 성능), Qwen-2.5-14B는 상대적으로 작은 모델 크기에도 더 높은 training compute 덕분에 EXAONE-3.5-32B보다 우수한 성능을 보여주었습니다. 이는 HRMCR 문제 해결에 단순한 모델 크기뿐만 아니라 고도화된 훈련 전략과 충분한 계산 자원이 필요하다는 것을 의미합니다.\"]}]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/zwexiw1HV09Jv4TBxegTm2enJsU.png?width=1920&height=464\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-4\"},c:[{t:\"strong\",a:{},c:[\"Conclusion\"]}]},{t:\"p\",a:{},c:[\"한국어 추론 평가 벤치마크의 부족을 해결하기 위해, 수학적 추론 평가를 위한 HRM8K와 문화적 상식 추론 평가를 위한 HRMCR을 소개합니다. 본 포스트에서는 해당 벤치마크를 활용해 다양한 모델을 평가하고, 한국어와 다국어 환경에서의 추론 능력을 분석했습니다. 이를 통해 다국어 추론 격차와 모델 성능에 있어 컴퓨팅 자원의 역할 등 여러 흥미로운 발견을 하였습니다.\"]},{t:\"p\",a:{},c:[\"이번 연구는 LLM의 한국어 추론 능력을 더욱 깊이 탐구할 수 있는 가능성을 보여줍니다. HRM8K와 HRMCR의 도입이 한국어 LLM 커뮤니티에 기여하고, 이 분야의 향후 연구에 도움이 되기를 기대합니다. 해당 벤치마크에 대해 궁금한 점이 있으시면 \",{t:\"a\",a:{href:\"mailto:spthsrbwls123@yonsei.ac.kr\"},c:[\"spthsrbwls123@yonsei.ac.kr\"]},\"로 언제든지 문의해 주시길 바랍니다.\"]}],toc:[{id:\"section-1\",text:\"부족한 한국어 추론 평가\"},{id:\"section-2\",text:\"HRM8K: 한국어 수학 추론 벤치마크\"},{id:\"section-3\",text:\"HRMCR: 한국 문화 추론 벤치마크\"},{id:\"section-4\",text:\"Conclusion\"}],images:[\"https://framerusercontent.com/images/S3Zoi4mqDZYNlrKU3fyv0jmm0TU.png?width=960&height=384\",\"https://framerusercontent.com/images/xgTxxJUEemISDHw5GbUP4Gfbpis.png?width=1920&height=560\",\"https://framerusercontent.com/images/dYyz2TNFkmzxMGMkUz9N10ZoG8.png?width=1920&height=608\",\"https://framerusercontent.com/images/Kyr8naGoNAYtH9yQNCF7Dcan04.png?width=960&height=394\",\"https://framerusercontent.com/images/vtmi9IjCFPYkUki9mHPv0m99mU.png?width=1920&height=432\",\"https://framerusercontent.com/images/zwexiw1HV09Jv4TBxegTm2enJsU.png?width=1920&height=464\"],readMins:12}];\nexport const __FramerMetadata__ = {\"exports\":{\"JOURNAL_1\":{\"type\":\"variable\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"__FramerMetadata__\":{\"type\":\"variable\"}}}\n//# sourceMappingURL=./GraphiteResearch1.map","export const JOURNAL_2=[{id:16,page:\"Journal 17\",slug:\"olaf-v2\",kind:\"research\",topic:\"Models\",title:\"원라인에이아이 추론특화 모델 OLAF v2를 소개합니다!\",originalTitle:\"원라인에이아이 추론특화 모델 OLAF v2를 소개합니다!\",summary:\"추론에 초점을 맞춘 OLAF v2 모델을 소개합니다\",language:\"ko\",date:\"Jan 13, 2025\",dateISO:\"2025-01-13\",source:\"https://onelineai.com/en/blog-research/about-olaf-v2\",path:\"/research/olaf-v2\",nodes:[{t:\"h2\",a:{id:\"section-1\"},c:[\"OLAF v2를 소개합니다.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/VEkSSXnieyW1bjITuai7duCtd9U.png?width=852&height=248\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]},\"Hugging Face 모델 공개: \",{t:\"a\",a:{href:\"https://huggingface.co/OLAResearch/OLAF2-14B\"},c:[\"https://huggingface.co/OLAResearch/OLAF2-14B\"]}]},{t:\"p\",a:{},c:[\"OnelineAI에서 독자적으로 개발한 Qwen2.5를 기반으로 한 한국어 언어 모델 OLAFv2를 소개합니다! OLAFv2는 14B와 1.5B 두 가지 버전으로 제공되며, 두 버전 모두 정교한 수학 및 STEM 문제를 해결하기 위한 추론 특화 모드 (Reasoning Mode)를 갖추고 있습니다. 또한, 최대 32K의 context length를 지원하여 RAG (Retrieval-Augmented Generation) 및 도구 기반 애플리케이션에 적합하도록 제작되었습니다. 모델 학습 과정에서는 반복적인 데이터 생성과 안전성 (Safety) 및 거부 (Refusal) 메커니즘에 중점을 두어 환각 (Hallucination)을 줄이고 신뢰도를 높였습니다.\"]},{t:\"h2\",a:{id:\"section-2\"},c:[\"Reasoning Mode\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/Ee7UevrMJOgD0HbTX8rBRJAag.png?width=852&height=458\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"OpenAI o1의 추론 메커니즘으로 거론되는 추론 시간 스케일링 (Inference-Time Scaling)의 발전에 힘입어, OLAFv2는 Reasoning mode에서 더욱 길고 세부적인 추론 과정을 생성하도록 설계되었습니다. 학습 시점의 컴퓨팅 자원 대신 테스트 시점의 컴퓨팅 자원 확장에 주력하는 추론 시간 스케일링으로, 소형 모델도 그 잠재력을 최대한 발휘할 수 있게 됩니다. 이는 대규모 모델을 배포하기 어려운 하드웨어 제약이 걸린 환경에서 특히 유리합니다. 자세한 내용은 최근에 포스팅한 \",{t:\"a\",a:{href:\"https://www.onelineai.com/en/blog/hrm8k\"},c:[\"HRM8K 블로그\"]},\"의 “\",{t:\"strong\",a:{},c:[\"Scaling Test-Time Compute\"]},\"” 섹션을 참고하시기 바랍니다.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/VaClVWmjZi1KlBUDgMHCrPKD9vQ.png?width=852&height=405\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"OLAFv2의 Reasoning mode는 복잡한 한국어 수학 추론 문제로 구성된 HRM8K 벤치마크에서 성능을 43.8에서 45.8로 크게 향상시켰습니다. 이 기능을 통해 소형 모델도 대형 모델에 근접한 추론 성능을 달성하면서, 자원이 제한된 소형 사내 서버 환경에서도 강력한 추론 기능을 제공하는 고품질 언어 모델을 활용할 수 있습니다.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-3\"},c:[\"Evaluation\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/XdspTopguO6JjjHQYds4Txz4J4I.png?width=852&height=384\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"Reasoning(추론), Knowledge(지식), Fluency(유창성) 이렇게 3가지의 카테고리에서 OLAF2 모델을 평가하였습니다.\"]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"Reasoning \"]},\"— \",{t:\"a\",a:{href:\"https://arxiv.org/abs/2501.02448\"},c:[\"HRM8K\"]}]},{t:\"p\",a:{},c:[\"→ HRM8K는 8,011개의 영어-한국어 수학 문제로 이루어진 벤치마크로, 다국어 수학적 추론 역량을 평가하기 위해 설계되었습니다. 이 벤치마크에는 영어 수학 벤치마크와 한국어 수학 경시대회 및 시험 등 다양한 출처의 문제가 포함되어 있어, 두 언어에서의 문제 해결 능력을 평가할 수 있습니다.\"]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"Knowledge \"]},\"— \",{t:\"a\",a:{href:\"https://arxiv.org/abs/2402.11548\"},c:[\"KMMLU\"]}]},{t:\"p\",a:{},c:[\"→ KMMLU는 한국어 언어 모델의 역량을 평가하기 위해 고안된 종합 벤치마크로, 45개 과목에 걸친 총 35,030개의 전문가 수준 객관식 문제 (Multiple-Choice Question Answering, MCQA)로 구성되어 있습니다. 이 벤치마크는 모델의 전문 분야별 추론 능력, 일반 지식 역량, 한국 문화적 지식을 중점적으로 평가합니다.\"]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"Fluency \"]},\"— \",{t:\"a\",a:{href:\"https://github.com/instructkr/LogicKor\"},c:[\"LogicKor\"]}]},{t:\"p\",a:{},c:[\"→ LogicKor는 모델의 사고 능력을 측정하기 위해 설계된 한국어 다중 도메인 추론 벤치마크로, 추론, 수학, 글쓰기, 코딩, 이해, 문법의 여섯 가지 핵심 영역을 다룹니다. LLM을 심사자로 활용하는 LLM-as-a-Judge 방식을 활용하여 각 영역에서 다양한 작업을 포함한 42개의 다중 턴 (Multi-turn) 프롬프트를 평가합니다. 이를 통해 LogicKor는 복잡하고 다양한 한국어 도전 과제를 처리하는 모델의 역량을 종합적으로 평가합니다.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/FDXlls9EudsDKuCibCEy0ysJuo.png?width=1472&height=930\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"em\",a:{},c:[\"*은 EXAONE3.5 Techincal Report에서 발췌한 점수를 의미함\",{t:\"br\",a:{},c:[]},\"**은 LogicKor 공식 리더보드에서 발췌한 점수를 의미함\",{t:\"br\",a:{},c:[]},\"그 외의 점수는 공식 구현 코드를 사용하여 재평가한 결과\"]}]},{t:\"h2\",a:{id:\"section-4\"},c:[\"Key Takeaways\"]},{t:\"h3\",a:{id:\"section-5\"},c:[\"OLAFv2\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/RYHf1hiQCvsRmxmGzqPdg6mbBKA.png?width=852&height=390\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"(Standard Mode)\"]},\": Standard mode에서 OLAFv2(14B 파라미터)는 \",{t:\"strong\",a:{},c:[\"43.8\"]},\"점을 HRM8K에서 기록하며, 크기가 두 배 이상 큰 EXAONE-3.5-32B-Instruct(41.4)를 능가하는 성능을 보여줍니다. 이는 OLAFv2가 전용 추론 모드를 사용하지 않아도 뛰어난 추론 능력을 갖추고 있음을 시사합니다. KMMLU에서는 OLAFv2가 \",{t:\"strong\",a:{},c:[\"54.21\"]},\"점을 기록하며, 훨씬 더 큰 모델인 Llama-3.1-70B-Instruct(60.83) 바로 아래에 위치하면서도 EXAONE-3.5-32B-Instruct(47.63)를 크게 앞섭니다. 또한, LogicKor에서는 \",{t:\"strong\",a:{},c:[\"8.51\"]},\"점을 기록해 EXAONE-3.5-32B-Instruct(9.06)에 이어 두 번째로 높은 평가 결과를 거두며, OLAFv2가 상대적으로 작은 크기에도 불구하고 유창하고 일관된 출력을 생성하는 뛰어난 능력을 보여줍니다.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/F0ycV9t0JFlVYilp1cHWUUrJR4.png?width=852&height=390\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"(Reasoning Mode)\"]},\": Reasoning mode에서 OLAFv2는 \",{t:\"strong\",a:{},c:[\"45.8\"]},\"점을 HRM8K 벤치마크에서 기록하며 Llama-3.1-70B-Instruct(45.6)와 Qwen2.5-32B-Instruct(44.4)와 같은 더 큰 모델들을 능가하는 최고 점수를 달성했습니다. 이는 Reasoning mode를 통한 추론 과정이 복잡한 추론 작업을 처리하는 데에 효과적이라는 것을 입증합니다.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h3\",a:{id:\"section-6\"},c:[\"OLAFv2-Mini\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/esS1S6F1UIDCIP6BjWx2hg43Es.png?width=852&height=390\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"(Standard Mode)\"]},\": Standard mode에서 OLAFv2-Mini는 \",{t:\"strong\",a:{},c:[\"35.9\"]},\"점을 HRM8K에서 달성하며, 소형 모델임에도 불구하고 뛰어난 추론 성능을 보여줍니다. KMMLU에서는 \",{t:\"strong\",a:{},c:[\"44.77\"]},\"점을 기록하며, EXAONE-3.5-2.4B-Instruct(42.39)와 같은 다른 소형 모델을 능가하며 강력하고 효율적인 지식 표현 능력을 입증합니다. LogicKor(유창성)에서는 \",{t:\"strong\",a:{},c:[\"7.4\"]},\"점을 기록하며, 소형 모델로서 합리적인 유창성을 제공하지만, 상위 대형 모델들보다는 약간 뒤처집니다.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/XwM0otM108VSCvAWMRx7nPvE5m0.png?width=852&height=334\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"(Reasoning Mode)\"]},\": Reasoning mode에서 OLAFv2-Mini는 \",{t:\"strong\",a:{},c:[\"38.0\"]},\"점을 HRM8K에서 기록하며, Reasoning mode가 소형 모델에서도 효과적으로 작동하면서 강력한 성능을 보여주었습니다. 이는 Reasoning mode와 테스트 시 계산 확장 (Test-time Scaling)의 이점을 더욱 잘 보여줍니다.\"]}],toc:[{id:\"section-1\",text:\"OLAF v2를 소개합니다.\"},{id:\"section-2\",text:\"Reasoning Mode\"},{id:\"section-3\",text:\"Evaluation\"},{id:\"section-4\",text:\"Key Takeaways\"},{id:\"section-5\",text:\"OLAFv2\"},{id:\"section-6\",text:\"OLAFv2-Mini\"}],images:[\"https://framerusercontent.com/images/VEkSSXnieyW1bjITuai7duCtd9U.png?width=852&height=248\",\"https://framerusercontent.com/images/Ee7UevrMJOgD0HbTX8rBRJAag.png?width=852&height=458\",\"https://framerusercontent.com/images/VaClVWmjZi1KlBUDgMHCrPKD9vQ.png?width=852&height=405\",\"https://framerusercontent.com/images/XdspTopguO6JjjHQYds4Txz4J4I.png?width=852&height=384\",\"https://framerusercontent.com/images/FDXlls9EudsDKuCibCEy0ysJuo.png?width=1472&height=930\",\"https://framerusercontent.com/images/RYHf1hiQCvsRmxmGzqPdg6mbBKA.png?width=852&height=390\",\"https://framerusercontent.com/images/F0ycV9t0JFlVYilp1cHWUUrJR4.png?width=852&height=390\",\"https://framerusercontent.com/images/esS1S6F1UIDCIP6BjWx2hg43Es.png?width=852&height=390\",\"https://framerusercontent.com/images/XwM0otM108VSCvAWMRx7nPvE5m0.png?width=852&height=334\"],readMins:5},{id:17,page:\"Journal 18\",slug:\"qwq-longcot\",kind:\"research\",topic:\"Data\",title:\"Releasing QwQ-LongCoT-130K\",originalTitle:\"Releasing QwQ-LongCoT-130K\",summary:\"A release note on a long chain-of-thought dataset.\",language:\"en\",date:\"Dec 6, 2024\",dateISO:\"2024-12-06\",source:\"https://onelineai.com/en/blog-research/qwq-longcot-130k\",path:\"/research/qwq-longcot\",nodes:[{t:\"p\",a:{},c:[\"Today, I’m excited to release \",{t:\"strong\",a:{},c:[\"QwQ-LongCoT-130K\"]},\", a SFT dataset designed for training O1-like large language models (LLMs). This dataset includes about 130k instances, each with responses generated using \",{t:\"strong\",a:{},c:[\"QwQ-32B-Preview\"]},\". The dataset is available under the \",{t:\"strong\",a:{},c:[\"Apache 2.0 license\"]},\", so feel free to use it as you like.\"]},{t:\"h3\",a:{id:\"section-1\"},c:[\"Dataset Construction\"]},{t:\"p\",a:{},c:[\"The challenging part of creating \",{t:\"strong\",a:{},c:[\"QwQ-LongCoT-130K\"]},\" was curating seed instructions that truly worth longer chain-of-thought reasoning. Simply put, I didn’t want to generate lengthy responses—spanning thousands of tokens—for simple prompts like, \",{t:\"em\",a:{},c:[\"“What color is the sky?”\"]},\" At the same time, I wanted them to be free of licensing issues. Accordingly, I collect seed-instructions using the following two methods.\"]},{t:\"p\",a:{},c:[\"Initially, I sourced data from the \",{t:\"strong\",a:{},c:[\"NuminaMath-CoT\"]},\" dataset, which contains a collection of 860K math questions and their corresponding answers. This dataset is licensed under Apache 2.0. To add diversity and include categories beyond math, I used the \",{t:\"strong\",a:{},c:[\"Magpie\"]},\" approach to extract questions from the QwQ-32B-Preview model. A common approach with Magpie involves inputting a blank space, sometimes with a user token, and expecting the model to generate a user query. However, with QwQ-32B-Preview, we observed that this method often leads the model to refuse to respond, frequently replying with something like: \",{t:\"em\",a:{},c:[\"“I’d be able to assist better if you provided more details.”\"]},\" Also using this approach we have little or no control over the instruction generated. So, in our experiments we use the following template:\"]},{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"pre\",a:{},c:[{t:\"span\",a:{},c:[\"import\"]},\" \",{t:\"span\",a:{},c:[\"random\"]},\"\\n\",{t:\"span\",a:{},c:[\"adjective\"]},\" = \",{t:\"span\",a:{},c:[\"random\"]},\".\",{t:\"span\",a:{},c:[\"choice\"]},{t:\"span\",a:{},c:[\"(\"]},{t:\"span\",a:{},c:[\"[\"]},{t:\"span\",a:{},c:['\"Physics\"']},{t:\"span\",a:{},c:[\",\"]},\" \",{t:\"span\",a:{},c:['\"Chemistry\"']},{t:\"span\",a:{},c:[\",\"]},\" \",{t:\"span\",a:{},c:['\"Biology\"']},{t:\"span\",a:{},c:[\",\"]},\" \",{t:\"span\",a:{},c:[\"...\"]},{t:\"span\",a:{},c:[\"]\"]},{t:\"span\",a:{},c:[\")\"]},\"\\n\",{t:\"span\",a:{},c:[\"subject\"]},\" = \",{t:\"span\",a:{},c:[\"random\"]},\".\",{t:\"span\",a:{},c:[\"choice\"]},{t:\"span\",a:{},c:[\"(\"]},{t:\"span\",a:{},c:[\"[\"]},\" \",{t:\"span\",a:{},c:['\"difficult\"']},{t:\"span\",a:{},c:[\",\"]},\" \",{t:\"span\",a:{},c:['\"tough\"']},{t:\"span\",a:{},c:[\",\"]},\" \",{t:\"span\",a:{},c:['\"long\"']},{t:\"span\",a:{},c:[\",\"]},\" \",{t:\"span\",a:{},c:['\"challenging\"']},{t:\"span\",a:{},c:[\",\"]},\" \",{t:\"span\",a:{},c:['\"tricky\"']},{t:\"span\",a:{},c:[\",\"]},\" \",{t:\"span\",a:{},c:[\"...\"]},{t:\"span\",a:{},c:[\"]\"]},{t:\"span\",a:{},c:[\")\"]},\"\\n\\n\",{t:\"span\",a:{},c:[\"This \"]},{t:\"span\",a:{},c:[\"is \"]},{t:\"span\",a:{},c:[\"one \"]},{t:\"span\",a:{},c:[\"{\"]},{t:\"span\",a:{},c:[\"adjective\"]},{t:\"span\",a:{},c:[\"}\"]},\" \",{t:\"span\",a:{},c:[\"{\"]},{t:\"span\",a:{},c:[\"subject\"]},{t:\"span\",a:{},c:[\"}\"]},\" \",{t:\"span\",a:{},c:[\"question\"]},\". \",{t:\"span\",a:{},c:[\"I\"]},{t:\"span\",a:{},c:[\"'ll first repeat the question word-by-word before I start to solve.\"]}]}]}]}]}]}]}]}]}]},{t:\"p\",a:{},c:[\"After collecting the seed instructions, I used QwQ-32B-Preview to generate one response for each instruction. Once the generation was complete, I applied simple rule-based filtering to remove responses containing phrases like \",{t:\"em\",a:{},c:['\"Sorry\"']},\" or \",{t:\"em\",a:{},c:['\"As an AI model.\"']},\" I also filtered out instances with excessive repetition of sentences and attempted to exclude those containing Chinese characters—though some may still remain. In any case, there is still room for further refinement.\"]},{t:\"h3\",a:{id:\"section-2\"},c:[\"Dataset Analysis\"]},{t:\"p\",a:{},c:[\"The dataset consists of 90k samples from NuminaMath and about 43k generated via Magpie. In my first effort with Magpie, I accidentally forgot to log the subjects used to generate each instruction, but in the figure below you can see the distributions of the ones I didn't forget (oops). I'm planning to add more Magpie data if I find some more computing resources.\",{t:\"br\",a:{},c:[]},{t:\"br\",a:{},c:[]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/nnqc1fud7NxYNApajI4pCBdQ.png?width=2250&height=1100\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"Below is a comparison of the length distribution of instances in the QwQ-LongCoT dataset, using the top_300k_longer_conversations subset from Magpie-Ultra as a baseline. For the readability of the plot, I excluded some outliers exceeding 20k characters from the QwQ-LongCoT dataset, although the longest sample had over 170k characters. From the plot, it is evident that QwQ-LongCoT generally contains longer instances.\",{t:\"br\",a:{},c:[]},{t:\"br\",a:{},c:[]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/PMY98cfZJzt3zfosP6kc1wbLbPk.png?width=989&height=590\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"h3\",a:{id:\"section-3\"},c:[\"Lessons learned from training with \",{t:\"strong\",a:{},c:[\"QwQ-LongCoT-130K\"]}]},{t:\"p\",a:{},c:['Well, I initially tried training with the dataset in a simple SFT setting, only to find that it does not work well. My random guess is that the thinking traces in QwQ-LongCoT include intentionally generating wrong statements and then fixing them. This \"intentionally generating wrong stuff\" seems to be bad for the model—I don’t have any evidence. I probably need different approaches to mask away the wrong traces during SFT or use RL. The model is still embarrassing to share, and I\\'m still trying some more training runs, so I hope to get a decent, shareable model soon.']}],toc:[{id:\"section-1\",text:\"Dataset Construction\"},{id:\"section-2\",text:\"Dataset Analysis\"},{id:\"section-3\",text:\"Lessons learned from training with QwQ-LongCoT-130K\"}],images:[\"https://framerusercontent.com/images/nnqc1fud7NxYNApajI4pCBdQ.png?width=2250&height=1100\",\"https://framerusercontent.com/images/PMY98cfZJzt3zfosP6kc1wbLbPk.png?width=989&height=590\"],readMins:3},{id:18,page:\"Journal 19\",slug:\"hrm8k\",kind:\"research\",topic:\"Evaluation\",title:\"Introducing HRM8K, the first Korean Math Reasoning Benchmark\",originalTitle:\"Introducing HRM8K, the first Korean Math Reasoning Benchmark\",summary:\"A benchmark designed to evaluate mathematical reasoning in Korean.\",language:\"en\",date:\"Dec 5, 2024\",dateISO:\"2024-12-05\",source:\"https://onelineai.com/en/blog-research/hrm8k\",path:\"/research/hrm8k\",nodes:[{t:\"p\",a:{},c:[\"We are excited to introduce \",{t:\"strong\",a:{},c:[\"HRM8K (HAE-RAE Math 8K)\"]},\", the first publicly available benchmark for mathematical reasoning in Korean. HRM8K comprises \",{t:\"strong\",a:{},c:[\"8,011 instances\"]},\", sourced through a combination of translations from established English benchmarks (e.g., GSM8K, MATH, NuminaMath, MMMLU) and original problems curated from existing Korean math exams by our team.\"]},{t:\"h3\",a:{id:\"section-1\"},c:[\"Benchmark Details\"]},{t:\"p\",a:{},c:[\"The \",{t:\"strong\",a:{},c:[\"HRM8K\"]},\" benchmark consists of two subsets.\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Korean School Math (KSM)\"]},\": This subset includes 1,428 challenging Korean mathematical problems from Korean examinations and competitions. The problems are manually captured as screenshots by a human labeler, processed through OCR using the GPT-4o API and cross-checked by two labelers.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/aikQI6AqRZT0rV6g90oboFFzNwk.png?width=1500&height=662\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Prior Sets\"]},\": This subset comprises 6,583 math problems translated from existing English benchmarks, including GSM8K, MATH, Numina-Math, and MMLU. For GSM8K, MATH, and Numina-Math, translations are done using the GPT-4o API, followed by a human quality check. For MMLU, we utilize the human-translated version provided by OpenAI (MMMLU).\"]}]}]},{t:\"h3\",a:{id:\"section-2\"},c:[\"Evaluation Settings\"]},{t:\"p\",a:{},c:[\"LLMs are initially prompted in the following manner for our experiments. In cases where system roles are not available, the \",{t:\"code\",a:{},c:[\"system_message\"]},\" is included in the user query.\"]},{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"pre\",a:{},c:[{t:\"span\",a:{},c:[\"system_message\"]},\" = \",{t:\"span\",a:{},c:['\"\"']},{t:\"span\",a:{},c:['\"Solve the given question. After solving the problem, state your final answer in the following format: $\\\\\\\\\\\\\\\\boxed{N}$.\"']},{t:\"span\",a:{},c:['\"\"']},\"\\n\",{t:\"span\",a:{},c:[\"problem\"]},\" = \",{t:\"span\",a:{},c:['\"\"']},\"\\n\\n\",{t:\"span\",a:{},c:[\"messages\"]},\" = \",{t:\"span\",a:{},c:[\"[\"]},\"\\n    \",{t:\"span\",a:{},c:[\"{\"]},{t:\"span\",a:{},c:['\"role\"']},{t:\"span\",a:{},c:[\":\"]},\" \",{t:\"span\",a:{},c:['\"system\"']},{t:\"span\",a:{},c:[\",\"]},\" \",{t:\"span\",a:{},c:['\"content\"']},{t:\"span\",a:{},c:[\":\"]},\" \",{t:\"span\",a:{},c:[\"system_message\"]},{t:\"span\",a:{},c:[\"}\"]},{t:\"span\",a:{},c:[\",\"]},\"\\n    \",{t:\"span\",a:{},c:[\"{\"]},{t:\"span\",a:{},c:['\"role\"']},{t:\"span\",a:{},c:[\":\"]},\" \",{t:\"span\",a:{},c:['\"user\"']},{t:\"span\",a:{},c:[\",\"]},\" \",{t:\"span\",a:{},c:['\"content\"']},{t:\"span\",a:{},c:[\":\"]},\" \",{t:\"span\",a:{},c:[\"problem\"]},\" + \",{t:\"span\",a:{},c:['\"Respond in Korean.\"']},{t:\"span\",a:{},c:[\"}\"]},\"\\n\",{t:\"span\",a:{},c:[\"]\"]},\"\\n\",{t:\"span\",a:{},c:[\"text\"]},\" = \",{t:\"span\",a:{},c:[\"tokenizer\"]},\".\",{t:\"span\",a:{},c:[\"apply_chat_template\"]},{t:\"span\",a:{},c:[\"(\"]},\"\\n    \",{t:\"span\",a:{},c:[\"messages\"]},{t:\"span\",a:{},c:[\",\"]},\"\\n    \",{t:\"span\",a:{},c:[\"tokenize\"]},\"=\",{t:\"span\",a:{},c:[\"False\"]},{t:\"span\",a:{},c:[\",\"]},\"\\n    \",{t:\"span\",a:{},c:[\"add_generation_prompt\"]},\"=\",{t:\"span\",a:{},c:[\"True\"]},\"\\n\",{t:\"span\",a:{},c:[\")\"]}]}]}]}]}]}]}]}]}]},{t:\"p\",a:{},c:[\"We include the “Respond in Korean” in our prompt, as the ability of interest is each model's capability to solve and explain math questions in Korean. Moreover, while using a low temperature is ideal for pass@1 settings, we observe that multiple LLMs tend to reason in their preferred language (e.g., English or Chinese) in such an environment. Accordingly, we use the following sampling parameters as our default setting unless otherwise specified.\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"temperature = 0.7\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"top_p = 0.95\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"min_tokens = 8\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"max_tokens = 2048\"]}]}]},{t:\"h3\",a:{id:\"section-3\"},c:[\"Performance Report\"]},{t:\"p\",a:{},c:[\"Here, we report the performance of existing LLMs on HRM8K.\"]},{t:\"p\",a:{},c:[\"To our surprise, we observe that the performance of Qwen2.5-72B-Instruct is on par with GPT-4o. GPT-4o-Mini and Llama-3.1-70B-Instruct follow the two models. Llama-3.1-405B-Instruct was excluded due to hardware constraints.\"]},{t:\"blockquote\",a:{},c:[{t:\"p\",a:{},c:[\"Frontier Models (Proprietary / Large & Open)\"]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/zsJKWQYbcd9vWqdTPIRe9Grad4.png?width=1854&height=262\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"We also present the evaluation results for smaller LLMs (<20B), including several models developed by Korean companies. Below are key details about the models:\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"VARCO-8B-Instruct\"]},\": A fine-tuned version of Llama-3.1-8B.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Solar Pro (preview) Instruct\"]},\": A derivative of Phi-3-Medium, scaled through model merging and further trained. Please note that this version of the model does not officially support Korean.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Exaone-3-7.8B-Instruct\"]},\": A model trained from scratch on 8T tokens, with a mix of Korean and English data.\"]}]}]},{t:\"p\",a:{},c:[\"In terms of performance, Qwen2.5-14B-Instruct leads with a score of 43.5, followed by Llama-3.1-8B-Instruct (39.4), Qwen2.5-7B-Instruct (37.0), and Exaone-3.0-7.8B-Instruct (36.02). Notably, despite not being specifically pre-trained on Korean data, Qwen2.5 and Llama-3.1 demonstrate competitive performance.\"]},{t:\"blockquote\",a:{},c:[{t:\"p\",a:{},c:[\"Open LLMs ( < 20B)\"]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/2MfWJxkP520MYNT6TzCXbF0Ao.png?width=2002&height=436\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"We further compare the performance of Qwen2.5-7B, Llama3.1-8B, and Exaone3-7.8B relative to their computational budget during pretraining. Following Kaplan et al. (2020), we estimate pretraining compute as 6\\xd7#parameters\\xd7#tokens. For brevity, we use ExaFLOPs to represent the scale of computational operations, where each unit corresponds to 10^{18} floating-point operations (FLOPs).\"]},{t:\"p\",a:{},c:[\"The table below shows that Exaone-3.0-7.8B-Instruct is trained with a relatively smaller compute budget than its counterparts. Notably, for Exaone-3.0-7.8B-Instruct, each unit of ExaFLOP compute during pretraining contributes to a score of 0.00058 on HRM8K, outperforming Llama-3.1-8B-Instruct and Qwen2.5-7B-Instruct in efficiency. We hypothesize that this is due to its focus on Korean data during pretraining data curation.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/OJmYU8zr99Q0yOiN3eYDJonxsJ8.png?width=1880&height=216\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"We are operating the HRM8K leaderboard \",{t:\"code\",a:{},c:[{t:\"a\",a:{href:\"https://github.com/guijinSON/HRM8K\"},c:[{t:\"strong\",a:{},c:[\"here\"]}]}]},\", if you are interested in performance of more models.\"]},{t:\"h3\",a:{id:\"section-4\"},c:[\"Scaling Test-Time Compute\"]},{t:\"p\",a:{},c:[\"Considering the recent hype on scaling test-time compute, we detail relevant experiments in this section. A common approach is training a dedicated LLM with Long-CoT (Chain-of-Thought) reasoning. A notable example is \",{t:\"strong\",a:{},c:[\"QwQ-preview\"]},\" by the Qwen Team. Interestingly, as shown in the table below, QwQ-preview underperforms compared to Qwen2.5-32B-Instruct on HRM8K. While little is known about the specific training recipe for QwQ-preview, our educated guess is that it is primarily trained on English or Chinese datasets, making it less effective for Korean questions. Please note that we observe the model respond in either English or Chinese for most instances, even while being prompted to explain in Korean.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/Fe2NX3w8tKhFUASr89BEA0ccVM4.png?width=1874&height=166\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"To mitigate the issue, we train two versions of Long-CoT models of our own: OLV-0.1 and OLV-0.2.\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"For \",{t:\"strong\",a:{},c:[\"OLV-0.1,\"]},\" we train Qwen2.5-7B to generate a two-step chain of CoT reasoning. In its first step, the model takes time to understand the question and plan a solution. In its second step, the solution is generated. \"]}]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"OLV-0.2\"]},\" is the highlight. We train it to generate a three-step CoT reasoning. To the above-mentioned steps, we add a final step where the model re-iterates its response to check whether it has made any mistakes. To our surprise, the model successfully learns to fix its own mistakes. \"]}]}]},{t:\"p\",a:{},c:[\"We detail the performance of Qwen2.5-7B-Instruct, OLV-0.1, and OLV-0.2 in the table below.  OLV models outperform their baseline greatly. Furthermore, the added re-iteration step successfully pushes the model further. \"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/Oi7M6mSTm7jtzGQdgM7tVn54vyo.png?width=1838&height=228\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"Below, we provide \",{t:\"strong\",a:{},c:[\"examples of OLV-0.2 self-correcting itself\"]},\". (Yes, these are cherry-picked examples, and OLV sometimes fixes correct responses.)\"]},{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"pre\",a:{},c:[\"### Question: Carla는 200GB 파일을 다운로드 중입니다. 보통 1분에 2GB를 다운로드할 수 있지만, 다운로드의 40%가 진행됐을 때 Windows가 업데이트 설치를 위해 재시작합니다. 이 과정은 20분이 걸립니다. 그러고 나서 Carla는 다운로드를 처음부터 다시 시작해야 합니다. 파일을 다운로드하는 데 총 얼마나 걸리나요?\\n### Answer: 160.0\\n### Response by OLV-0.2:\\n\\n\",{t:\"span\",a:{},c:[\"[Understanding Question]\"]},\"\\n카를라는 200GB 파일을 다운로드 중입니다.\\n\\n1. \",{t:\"span\",a:{},c:[\"**다운로드 속도**\"]},\": 그녀는 분당 2GB의 속도로 파일을 다운로드합니다.\\n2. \",{t:\"span\",a:{},c:[\"**재시작 이벤트**\"]},\": 다운로드의 40%가 완료될 때, Windows가 업데이트를 설치하기 위해 그 다운로드를 재시작합니다.\\n3. \",{t:\"span\",a:{},c:[\"**재시작 시간**\"]},\": 이 재시작 과정은 20분이 걸립니다.\\n4. \",{t:\"span\",a:{},c:[\"**재시작 후**\"]},\": 재시작 후에는 다운로드를 처음부터 다시 시작합니다.\\n\\n질문은 200GB 파일을 다운로드하는 데 걸리는 총 시간을 묻고 있습니다.\\n\\n\",{t:\"span\",a:{},c:[\"[Solving Question]\"]},\"\\n먼저, 파일의 40%를 다운로드하는 데 걸리는 시간을 계산해 보겠습니다.\\n200GB 파일의 40%는 0.4 * 200 = 80GB입니다.\\n분당 2GB의 속도로 80GB를 다운로드하는 데 걸리는 시간은 80 / 2 = 40분입니다.\\n이때 Windows가 업데이트를 설치하기 위해 재시작하는 시간 20분을 더하면 40 + 20 = 60분이 됩니다.\\n이제 남은 60%의 파일을 다운로드하는 데 걸리는 시간을 계산해 보겠습니다.\\n200GB 파일의 60%는 0.6 * 200 = 120GB입니다.\\n분당 2GB의 속도로 120GB를 다운로드하는 데 걸리는 시간은 120 / 2 = 60분입니다.\\n따라서 파일을 다운로드하는 데 걸리는 총 시간은 60 + 60 = 120분입니다.\\n따라서 파일을 다운로드하는 데 걸리는 총 시간은 \\\\boxed{120}분입니다.\\n\\n\",{t:\"span\",a:{},c:[\"[Re-Iterating Solution]\"]},\"\\n\\n\"]}]}]}]}]}]}]}]}]},{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"pre\",a:{},c:[\"### Question: Candice는 커피숍으로 출근하기 전에 80개의 포스트잇을 가방에 넣었습니다. 그녀는 가는 길에 가게에 들러 포스트잇 한 패키지를 구입했습니다. 직장에서 그녀는 220개의 다른 커피 컵 각각에 하나씩 포스트잇을 붙였습니다. 전체적으로 23개의 포스트잇이 남아있었다면, 그녀가 구입한 패키지에는 포스트잇이 몇 개 들어 있었나요?\\n### Answer: 163.0\\n### Response by OLV-0.2:\\n\\n\",{t:\"span\",a:{},c:[\"[Understanding Question]\"]},\"\\n캐니스는 커피숍으로 가기 위해 가방에 80개의 포스트잇을 넣었습니다. \\n\\n1. 그녀는 출근하기 전에 가방에 80개의 포스트잇을 가지고 있었습니다.\\n2. 그녀는 가게에 가서 포스트잇 한 패키지를 구입했습니다.\\n3. 직장에서 그녀는 220개의 커피 컵 각각에 포스트잇 하나씩 붙였습니다.\\n4. 그녀가 모든 포스트잇을 사용한 후에는 총 23개의 포스트잇이 남아 있었습니다.\\n\\n질문은 그녀가 가게에서 구입한 포스트잇 패키지에 몇 개가 있었는지를 묻고 있습니다. \\n\\n이를 찾기 위해 우리는 다음을 추적해야 합니다:\\n- 처음의 포스트잇 수: 80\\n- 직장에서 붙인 포스트잇 수: 220\\n- 최종적으로 남은 포스트잇 수: 23\\n\\n이 정보를 사용하여 그녀가 구입한 포스트잇 패키지의 수를 계산할 수 있습니다.\\n\\n\",{t:\"span\",a:{},c:[\"[Solving Question]\"]},\"\\n캐니스는 처음에 80개의 포스트잇을 가지고 있었습니다.\\n그녀는 가게에서 포스트잇 한 패키지를 구입했습니다.\\n그녀는 직장에서 220개의 커피 컵 각각에 포스트잇 하나씩 붙였습니다.\\n그녀는 총 23개의 포스트잇이 남아 있었습니다.\\n따라서 그녀가 가진 포스트잇의 총 수는 80 + 패키지에 있는 포스트잇 수 - 220 + 23입니다.\\n패키지에 있는 포스트잇 수를 x라고 하겠습니다.\\n그러면 80 + x - 220 + 23 = 23이므로 x = 114입니다.\\n따라서 그녀가 구입한 포스트잇 패키지는 \\\\boxed{114}개였습니다.\\n\\n\",{t:\"span\",a:{},c:[\"[Re-Iterating Solution]\"]},\"\\n\\n\"]}]}]}]}]}]}]}]}]},{t:\"h3\",a:{id:\"section-5\"},c:[{t:\"br\",a:{},c:[]}]},{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"div\",a:{},c:[{t:\"pre\",a:{},c:[\"### Question: 볼록오각형 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" ABCDE \",{t:\"span\",a:{},c:[\"\\\\)\"]},\" 에서 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" BE \",{t:\"span\",a:{},c:[\"\\\\)\"]},\" 와 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" BC \",{t:\"span\",a:{},c:[\"\\\\)\"]},\" 가 각각 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" CD \",{t:\"span\",a:{},c:[\"\\\\)\"]},\" 와 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" AD \",{t:\"span\",a:{},c:[\"\\\\)\"]},\" 에 평행하고 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" BC = ED \",{t:\"span\",a:{},c:[\"\\\\)\"]},\", \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" AB = CD \",{t:\"span\",a:{},c:[\"\\\\)\"]},\" 이다. \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" \\\\angle BCD = 130^\\\\circ \",{t:\"span\",a:{},c:[\"\\\\)\"]},\" 이고 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" \\\\angle ACE = x^\\\\circ \",{t:\"span\",a:{},c:[\"\\\\)\"]},\" 일 때, \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" x \",{t:\"span\",a:{},c:[\"\\\\)\"]},\" 를 구하여라.\\n### Answer: 80\\n### Response by OLV-0.2:\\n\\n\",{t:\"span\",a:{},c:[\"[Understanding Question]\"]},\"\\n1. \",{t:\"span\",a:{},c:[\"**문제 이해하기**\"]},\": 먼저 문제 설명을 주의 깊게 읽어 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" ABCDE \",{t:\"span\",a:{},c:[\"\\\\)\"]},\" 다각형의 기하학적 관계를 이해할 것입니다. \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" BE \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"와 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" BC \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"가 각각 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" CD \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"와 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" AD \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"에 평행하다는 점을 주목하며, 이는 각도 사이의 관계를 설정하는 데 도움이 될 것입니다.\\n\\n2. \",{t:\"span\",a:{},c:[\"**주어진 각도 확인하기**\"]},\": \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" \\\\angle BCD = 130^\\\\circ \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"라는 것을 알고 있습니다. 이 각도는 계산에서 중요하므로, 나중에 참조할 수 있도록 기록해 두겠습니다.\\n\\n3. \",{t:\"span\",a:{},c:[\"**평행선의 성질 사용하기**\"]},\": \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" BC \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"가 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" AD \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"에 평행하므로, 교차 내각을 적용할 수 있습니다. 이로 인해 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" \\\\angle BCD \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"와 관련된 각도를 찾아보겠습니다.\\n\\n4. \",{t:\"span\",a:{},c:[\"**다른 각도 계산하기**\"]},\": \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" \\\\angle BCD = 130^\\\\circ \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"를 알고 있으므로, \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" \\\\angle ACD \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"를 찾을 수 있습니다. \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" ACD \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"는 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" BCD \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"와 보각 관계에 있으므로, 보각의 성질을 사용하여 이 각도를 계산하겠습니다.\\n\\n5. \",{t:\"span\",a:{},c:[\"**삼각형 \"]},{t:\"span\",a:{},c:[\"\\\\(\"]},{t:\"span\",a:{},c:[\" ACE \"]},{t:\"span\",a:{},c:[\"\\\\)\"]},{t:\"span\",a:{},c:[\" 분석하기**\"]},\": 다음으로 삼각형 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" ACE \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"에 집중하겠습니다. \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" AC = AE \",{t:\"span\",a:{},c:[\"\\\\)\"]},\" (주어진 조건으로부터 유도됨)라는 것을 알고 있으므로, 이등변 삼각형의 성질을 사용할 수 있습니다.\\n\\n6. \",{t:\"span\",a:{},c:[\"**삼각형 각의 합 이용하기**\"]},\": 모든 삼각형에서 각의 합은 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" 180^\\\\circ \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"라는 것을 기억하겠습니다. \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" ACE \",{t:\"span\",a:{},c:[\"\\\\)\"]},\" 삼각형에서 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" \\\\angle CAE \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"와 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" \\\\angle AEC \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"를 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" \\\\angle ACE \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"에 연관시켜 표현하겠습니다.\\n\\n7. \",{t:\"span\",a:{},c:[\"**방정식 설정하기**\"]},\": 삼각형 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" ACE \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"의 각도를 포함하는 방정식을 작성하고, 이를 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" \\\\angle ACE \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"에 대해 풀겠습니다.\\n\\n8. \",{t:\"span\",a:{},c:[\"**최종 계산하기**\"]},\": 마지막으로, 방정식을 단순화하여 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" \\\\angle ACE \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"의 값을 찾겠습니다. 모든 계산을 주의 깊게 수행하여 정확성을 보장하도록 하겠습니다.\\n\\n\",{t:\"span\",a:{},c:[\"[Solving Question]\"]},\"\\n다음 문제를 해결해 보겠습니다:\\n볼록 오각형 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" ABCDE \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"에서, \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" BE \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"와 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" BC \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"는 각각 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" CD \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"와 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" AD \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"에 평행하고, \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" BC = ED \",{t:\"span\",a:{},c:[\"\\\\)\"]},\", \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" AB = CD \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"입니다. \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" \\\\angle BCD = 130^\\\\circ \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"이고, \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" \\\\angle ACE = x^\\\\circ \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"일 때, \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" x \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"의 값을 구해보겠습니다.\\n\\n1. \",{t:\"span\",a:{},c:[\"**주어진 조건을 이해하고 활용하기:**\"]},\"\\n    - \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" BC \\\\parallel AD \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"이고 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" BC = ED \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"입니다.\\n    - \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" AB = CD \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"입니다.\\n    - \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" \\\\angle BCD = 130^\\\\circ \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"입니다.\\n\\n2. \",{t:\"span\",a:{},c:[\"**평행선의 성질을 사용하여 각도 결정하기:**\"]},\"\\n    - \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" BC \\\\parallel AD \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"이므로, 교차 내부 각이 같다는 성질을 사용할 수 있습니다:\\n    \",{t:\"span\",a:{},c:[\"\\\\[\"]},\"\\n    \\\\angle BCD = \\\\angle ACD = 130^\\\\circ\\n    \",{t:\"span\",a:{},c:[\"\\\\]\"]},\"\\n    - 이는 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" \\\\angle BCD \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"와 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" \\\\angle ACD \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"가 선형 쌍을 이루기 때문입니다.\\n\\n3. \",{t:\"span\",a:{},c:[\"**\"]},{t:\"span\",a:{},c:[\"\\\\(\"]},{t:\"span\",a:{},c:[\" \\\\angle ACE \"]},{t:\"span\",a:{},c:[\"\\\\)\"]},{t:\"span\",a:{},c:[\"를 찾기 위한 설정:**\"]},\"\\n    - \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" AB = CD \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"이고 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" BC = ED \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"이므로, 우리는 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" \\\\angle ACE \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"를 찾아야 합니다.\\n\\n4. \",{t:\"span\",a:{},c:[\"**이등변 삼각형의 성질 사용하기:**\"]},\"\\n    - \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" AB = CD \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"이고 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" BC = ED \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"이므로, 삼각형 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" ACE \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"는 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" AC = AE \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"인 이등변 삼각형입니다. \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" \\\\angle ACE \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"는 이등변 삼각형의 꼭짓점 각입니다.\\n\\n5. \",{t:\"span\",a:{},c:[\"**\"]},{t:\"span\",a:{},c:[\"\\\\(\"]},{t:\"span\",a:{},c:[\" \\\\angle ACE \"]},{t:\"span\",a:{},c:[\"\\\\)\"]},{t:\"span\",a:{},c:[\" 계산하기:**\"]},\"\\n    - 삼각형의 각의 합은 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" 180^\\\\circ \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"입니다. 이등변 삼각형 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" ACE \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"에서:\\n    \",{t:\"span\",a:{},c:[\"\\\\[\"]},\"\\n    \\\\angle CAE = \\\\angle AEC = \\\\frac{180^\\\\circ - \\\\angle ACE}{2}\\n    \",{t:\"span\",a:{},c:[\"\\\\]\"]},\"\\n    - \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" \\\\angle BCD = 130^\\\\circ \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"이므로, \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" \\\\angle ACD = 130^\\\\circ \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"입니다. 따라서:\\n    \",{t:\"span\",a:{},c:[\"\\\\[\"]},\"\\n    \\\\angle ACE = 180^\\\\circ - \\\\angle ACD = 180^\\\\circ - 130^\\\\circ = 50^\\\\circ\\n    \",{t:\"span\",a:{},c:[\"\\\\]\"]},\"\\n\\n6. \",{t:\"span\",a:{},c:[\"**결론:**\"]},\"\\n    - 따라서 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" \\\\angle ACE \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"의 값은:\\n    \",{t:\"span\",a:{},c:[\"\\\\[\"]},\"\\n    \\\\boxed{50^\\\\circ}\\n    \",{t:\"span\",a:{},c:[\"\\\\]\"]},\"\\n\\n\",{t:\"span\",a:{},c:[\"[Re-Iterating Solution]\"]},\"\\n제 답변을 검토하며, 문제가 있는지 살펴보겠습니다. \\n\\n1. 주어진 조건을 바탕으로 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" BC \\\\parallel AD \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"이고, \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" AB = CD \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"임을 사용하여 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" \\\\angle BCD = 130^\\\\circ \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"라는 점을 확인했습니다. 이 각이 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" \\\\angle ACD \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"와 같은 이유는 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" BC \\\\parallel AD \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"에 따른 것입니다. 이 부분은 맞습니다.\\n\\n2. 그러나 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" \\\\angle ACD \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"를 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" 130^\\\\circ \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"라고 잘못 언급한 부분을 발견했습니다. 실제로는 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" \\\\angle ACD \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"가 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" \\\\angle BCD \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"와는 별개의 각도이므로, \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" \\\\angle ACD \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"는 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" 180^\\\\circ - 130^\\\\circ = 50^\\\\circ \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"여야 합니다.\\n\\n3. 따라서 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" \\\\angle ACE \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"를 구하는 과정에서 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" \\\\angle ACD = 130^\\\\circ \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"를 잘못 사용하여 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" \\\\angle ACE = 50^\\\\circ \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"라고 잘못 도출했습니다. 이는 주어진 정보와 모순되므로, 정답은 다음과 같이 수정해야 합니다.\\n\\n검토 결과 풀이에는 문제가 있었습니다. 따라서 수정된 정답은 \",{t:\"span\",a:{},c:[\"\\\\(\"]},\" \\\\angle ACE = 80^\\\\circ \",{t:\"span\",a:{},c:[\"\\\\)\"]},\"\\n\\n\"]}]}]}]}]}]}]}]}]},{t:\"h3\",a:{id:\"section-6\"},c:[\"Final Remarks\"]},{t:\"p\",a:{},c:[\"The OLV series will take some time to be made public as we are still actively making progress for longer and more dynamic reasoning chains. We just wanted to share our initial results to provide a glimpse of what we are working on. HRM8K, however, will be released soon (during December). If you want your models evaluated before the release, please contact us at \",{t:\"code\",a:{},c:[\"spthsrbwls123@yonsei.ac.kr\"]},\". \"]}],toc:[{id:\"section-1\",text:\"Benchmark Details\"},{id:\"section-2\",text:\"Evaluation Settings\"},{id:\"section-3\",text:\"Performance Report\"},{id:\"section-4\",text:\"Scaling Test-Time Compute\"},{id:\"section-5\",text:\"\"},{id:\"section-6\",text:\"Final Remarks\"}],images:[\"https://framerusercontent.com/images/aikQI6AqRZT0rV6g90oboFFzNwk.png?width=1500&height=662\",\"https://framerusercontent.com/images/zsJKWQYbcd9vWqdTPIRe9Grad4.png?width=1854&height=262\",\"https://framerusercontent.com/images/2MfWJxkP520MYNT6TzCXbF0Ao.png?width=2002&height=436\",\"https://framerusercontent.com/images/OJmYU8zr99Q0yOiN3eYDJonxsJ8.png?width=1880&height=216\",\"https://framerusercontent.com/images/Fe2NX3w8tKhFUASr89BEA0ccVM4.png?width=1874&height=166\",\"https://framerusercontent.com/images/Oi7M6mSTm7jtzGQdgM7tVn54vyo.png?width=1838&height=228\"],readMins:10},{id:19,page:\"Journal 20\",slug:\"krx-competition\",kind:\"research\",topic:\"Finance\",title:\"2024 KRX 금융 언어 모델 경진대회\",originalTitle:\"2024 KRX 금융 언어 모델 경진대회\",summary:\"금융 언어 모델 경진대회의 과제와 접근 방법\",language:\"ko\",date:\"Oct 7, 2024\",dateISO:\"2024-10-07\",source:\"https://onelineai.com/en/blog-research/2024-krx-%EA%B8%88%EC%9C%B5-%EC%96%B8%EC%96%B4-%EB%AA%A8%EB%8D%B8-%EA%B2%BD%EC%A7%84%EB%8C%80%ED%9A%8C\",path:\"/research/krx-competition\",nodes:[{t:\"img\",a:{src:\"https://framerusercontent.com/images/rjDs9UpYbxCzsShuxyA0bw5mS84.png?width=1754&height=2481\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"h3\",a:{id:\"section-1\"},c:[\"2024 KRX 금융 언어 모델 경진대회\"]},{t:\"p\",a:{},c:[\"OneLineAI가 KRX(한국거래소)에서 주최하는 'KRX 금융 언어 모델 경진대회'의 운영을 맡게 되었습니다. 이번 대회는 KRX-Bench라는 한국어 금융 벤치마크 생성 파이프라인으로 만들어진 데이터셋에서 높은 점수를 얻는 것을 목표로 합니다. 참가자들은 최신 금융 NLP 연구 성과를 활용하고, 금융 시장의 복잡성과 역동성을 반영한 고품질 데이터셋 및 학습 방법을 통해 금융 특화 언어 모델을 개발하게 됩니다.\"]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"OneLineAI\"]},\"와 \",{t:\"strong\",a:{},c:[\"KRX\"]},'가 공동으로 작업한 \"',{t:\"strong\",a:{},c:[\"KRX-Bench: Automating Financial Benchmark Creation via Large Language Models\"]},'\" 논문에서 제안한 KRX-Bench 벤치마크 생성 파이프라인을 통해 벤치마크 리더보드 데이터셋을 만들었습니다. 이 논문은 ',{t:\"strong\",a:{},c:[\"LREC-COLING 2024 FinNLP\"]},\"에서 발표했습니다.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"[제 3 회 KRX 금융언어모델 경진대회]\"]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"한국어 금융 벤치마크 생성 파이프라인 KRX-Bench으로 생성된 벤치마크 데이터셋에서 고득점을 목표로 금융 특화 언어 모델 개발\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"최신 금융 NLP 연구 및 금융 시장의 복잡성과 역동성을 반영한 고품질 데이터셋을 바탕으로 새로운 언어 모델 개발\"]}]}]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"[대회 사이트 링크]\"]}]},{t:\"p\",a:{},c:[{t:\"a\",a:{href:\"https://krxbench.koscom.co.kr/home\"},c:[\"https://krxbench.koscom.co.kr/home\"]}]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"[참가자격]\"]}]},{t:\"p\",a:{},c:[\"개인(학생•직장인 등) 및 법인(스타트업 등) 구분 없이 참여 가능\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"[시상 내역]\"]}]},{t:\"p\",a:{},c:[\"총 상금 6,000만원\"]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"최우수상\"]},\"\\xa0(1팀) - 3,000만원\"]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"우수상\"]},\"\\xa0(2팀) - 각 1,000만원\"]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"장려상\"]},\"\\xa0(2팀) - 각 500만원\"]},{t:\"p\",a:{},c:[\"※ 최우수상•우수상 수상팀 KRX 신입사원 채용 서류 전형 면제\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"[본선 진출 혜택]\"]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"AWS의 LLM 개발 Tool(SageMaker 등) 교육 무상 제공\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"AWS Cloud(GPU 등) 지원\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"본선 진출 팀(30팀)에게 활동비 50만원 지급\"]}]}]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"[대회 일정]\"]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"예선\"]},\": 2024년 10월 14일 ~ 2024년 11월 7일\\xa0※ 11월 5일 예선 서류 마감\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"예선 결과 발표\"]},\": 2024년 11월 8일\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"AWS 교육\"]},\": 2024년 11월 11일 ~ 12일\\xa0※ 본선 진출 30팀 대상\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"본선\"]},\": 2024년 11월 13일 ~ 12월 6일\\xa0※ 12월 5일 본선 서류 마감\"]}]}]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"[기타 문의사항]\"]}]},{t:\"p\",a:{},c:[\"경진대회 홈페이지 내 Slack 채널 이용\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"[주최 / 협찬 / 주관]\"]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"주최: 한국거래소(KRX)\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"협찬: 코스콤(Koscom), AWS\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"주관: 원라인에이아이(OneLineAI)\"]}]}]}],toc:[{id:\"section-1\",text:\"2024 KRX 금융 언어 모델 경진대회\"}],images:[\"https://framerusercontent.com/images/rjDs9UpYbxCzsShuxyA0bw5mS84.png?width=1754&height=2481\"],readMins:2},{id:20,page:\"Journal 21\",slug:\"synthetic-data-self-instruct\",kind:\"research\",topic:\"Data\",title:\"금융 도메인 언어모델 학습을 위한 합성데이터 생성 기법 Part1: Self-Instruct\",originalTitle:\"금융 도메인 언어모델 학습을 위한 합성데이터 생성 기법 Part1: Self-Instruct\",summary:\"금융 도메인을 위한 Self-Instruct 합성 데이터 생성\",language:\"ko\",date:\"Sep 16, 2024\",dateISO:\"2024-09-16\",source:\"https://onelineai.com/en/blog-research/synthetic-data-part1-self-instruct\",path:\"/research/synthetic-data-self-instruct\",nodes:[{t:\"blockquote\",a:{},c:[{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"이 글은 \",{t:\"a\",a:{href:\"https://arxiv.org/abs/2212.10560\"},c:[\"‘\",{t:\"strong\",a:{},c:[\"Self-Instruct: Aligning Language Models with Self-Generated Instructions(2023)’\"]}]},{t:\"strong\",a:{},c:[\" \"]},\"논문을 기반으로 언어모델 학습을 위한 합성데이터 생성 기법을 설명하고 있습니다.\"]}]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"해당 기법으로 원라인에이아이가 생성한 데이터들은 \",{t:\"strong\",a:{},c:[\"허깅페이스 \"]},{t:\"a\",a:{href:\"https://huggingface.co/HAERAE-HUB\"},c:[{t:\"strong\",a:{},c:[\"HAE-RAE Hub\"]}]},\"에서 확인하실 수 있습니다.\"]}]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"특정 도메인 학습데이터는 \",{t:\"a\",a:{href:\"https://www.onelineai.com/contact\"},c:[{t:\"strong\",a:{},c:[\"원라인에이아이 AI팀\"]}]},\"에게 문의주시기 바랍니다.\"]}]}]}]},{t:\"h2\",a:{id:\"section-1\"},c:[{t:\"strong\",a:{},c:[\"금융 AI 모델 개발의 초석, 데이터\"]}]},{t:\"p\",a:{},c:[\"금융 분야에서 AI의 중요성은 날로 커지고 있습니다. 주식 시장 예측, 신용 평가, 사기 탐지 등 다양한 영역에서 AI는 이미 핵심적인 역할을 하고 있지만, AI 모델을 개발하는 과정에서 많은 기업들이 ‘데이터’ 라는 공통된 난관에 부딪힙니다.\"]},{t:\"p\",a:{},c:[\"예를 들어, 새로운 유형의 금융 사기를 탐지하는 AI를 개발한다고 가정해봅시다. 이를 위해서는 다양한 사기 패턴과 정상 거래 데이터가 필요합니다. 하지만 실제 사기 데이터는 희소하고, 개인정보 보호 문제로 인해 접근이 제한적입니다. 또한 새로운 유형의 사기라면 그에 대한 데이터는 아예 존재하지 않을 수도 있습니다. 이런 상황에서 어떻게 효과적인 AI 모델을 개발할 수 있을까요?\"]},{t:\"p\",a:{},c:[\"바로 여기서 synthetic data, 즉 '합성 데이터'의 중요성이 대두됩니다. 합성 데이터란 실제 데이터의 특성을 모방하여 인공적으로 생성한 데이터를 말합니다. 이는 단순히 무작위로 만들어낸 가짜 데이터가 아니라 실제 데이터의 복잡한 패턴과 관계를 학습한 AI 모델이 생성한, 실제와 매우 유사하지만 완전히 새로운 데이터입니다.\"]},{t:\"p\",a:{},c:[\"합성 데이터는 다음과 같은 장점을 가지며 ‘데이터 부족’ 문제에 대한 해결책으로 떠오르고 있습니다:\"]},{t:\"ol\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"데이터 부족 문제 해결: 희소한 케이스나 새로운 시나리오에 대한 데이터를 대량으로 생성할 수 있습니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"개인정보 보호: 실제 고객 데이터를 사용하지 않기 때문에 개인정보 문제에서 자유롭습니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"다양성 확보: 다양한 조건과 시나리오를 반영한 데이터를 생성할 수 있어, 모델의 일반화 능력을 향상시킬 수 있습니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"비용 절감: 실제 데이터 수집에 드는 시간과 비용을 크게 줄일 수 있습니다.\"]}]}]},{t:\"h2\",a:{id:\"section-2\"},c:[{t:\"strong\",a:{},c:[\"금융 도메인 언어 모델 학습을 위한 합성데이터 생성 기법\"]}]},{t:\"p\",a:{},c:[\"본 시리즈는 금융 분야에서 활용할 수 있는 인공지능 언어 모델의 학습을 유명 합성데이터 생성 기법을 다룰 예정입니다. 합성데이터 생성 기술 3가지를 살펴보면서 합성데이터의 중요성, 다양한 생성 기법, 그리고 이를 통한 언어 모델의 학습 효과 등을 세부적으로 탐구할 것입니다. 각 포스팅은 이론적 배경부터 실제 적용 사례까지 폭넓게 다루어, 본 시리즈를 통해 금융 분야의 AI 전문가들과 관심 있는 독자들에게 유용한 인사이트를 전달하고자 합니다.\"]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"Timeline\"]}]},{t:\"ol\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"SELF-INSTRUCT: Aligning Language Models with Self-Generated Instructions (현 포스팅)\"]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"WizardLM: Empowering Large Language Models to Follow Complex Instructions (\",{t:\"a\",a:{href:\"https://www.onelineai.com/blog/synthetic-data-part2-wizardlm\"},c:[\"Link\"]},\")\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"Cosmopedia: how to create large-scale synthetic data for pre-training (\",{t:\"a\",a:{href:\"https://www.onelineai.com/blog/synthetic-data-part3-cosmopedia\"},c:[\"Link\"]},\")\"]}]}]},{t:\"h2\",a:{id:\"section-3\"},c:[{t:\"strong\",a:{},c:[\"SELF-INSTRUCT\"]}]},{t:\"p\",a:{},c:['첫 시작으로 2023년 3월, 세상을 뜨겁게 달군 Stanford의 Alpaca 학습 데이터 생성 방법으로 널리 알려진 \"SELF-INSTRUCT: Aligning Language Models with Self-Generated Instructions\"에 대해서 알아보겠습니다.']},{t:\"p\",a:{},c:[\"이 논문의 핵심 아이디어는 대규모 언어 모델(LLM)을 사용하여 스스로 지시사항(instruction)과 그에 따른 입출력 예시를 생성하게 하는 것입니다. 이 과정을 통해 다양한 태스크에 대한 대규모 데이터셋을 사람의 노력없이 자동으로 생성할 수 있게 됩니다.\"]},{t:\"p\",a:{},c:[\"Self-Instruct의 전반적인 프로세스는 다음과 같습니다:\"]},{t:\"ol\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"사람이 작성한 (instruction, instance) 쌍으로 이루어져 있는 175개 가량의 소수 seed task로 시작합니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"LLM이 seed task를 in-context example로 하여 새로운 instruction 및 instance를 생성합니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"생성된 데이터의 품질 및 중복 제거 등의 후처리를 진행합니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"이 과정을 반복하여 대규모 데이터셋을 구축합니다.\"]}]}]},{t:\"p\",a:{},c:[\"이러한 프로세스로 GPT3를 사용한 실험에서, 생성된 데이터로 파인튜닝한 모델은 기존 GPT3보다 instruction-following 능력이 33% 향상되었습니다. 또한 전문가가 작성한 새로운 태스크에 대해서도 우수한 성능을 보였습니다.\"]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"[SELF-INSTRUCT: AI가 스스로 학습하는 혁신적 방법론]\"]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/kKq1iXY0Q8NMeCmsLawJ86NoUA.png?width=4404&height=1379\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"Self-Instruct 방법론의 핵심은 대규모 언어 모델(LLM)을 활용하여 자체적으로 instruction(지시사항)과 그에 따른 instance(입출력 예시)를 생성하는 것입니다. 이 과정을 통해 다양한 태스크에 대한 대규모 데이터셋을 자동으로 구축할 수 있습니다. 앞서 살펴본 Self-Instruct의 전반적인 프로세스에 대해 자세하게 알아보겠습니다.\"]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"Step 0. Seed Task 설정\"]}]},{t:\"p\",a:{},c:[\"연구팀이 직접 작성한 175개의 task(instruction + instance)를 seed task로 설정하였습니다. 이 초기 task들은 다양한 NLP 작업을 포함하며, 향후 추가적으로 생성될 instruction 세트에 대한 지침이 되어줍니다.\"]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"Step 1. Instruction 생성\"]}]},{t:\"p\",a:{},c:[\"instruction 생성을 위해 LLM(GPT3 사용)에게 8개의 task instruction을 in-context 예시로 제시합니다. 이 중 6개는 연구팀이 작성한 task, 2개는 이전 단계에서 모델이 생성한 task에서 가져옴으로써 다양성 증진과 함께 instruction을 생성합니다.\"]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"Step 2. 분류 Task 식별\"]}]},{t:\"p\",a:{},c:[\"생성된 지시사항의 분류 task 여부를 확인합니다. 이는 분류 task 여부에 따라 instance 생성 방식을 결정하기 때문에 중요합니다. 분류 task 판별을 위해 seed task에 존재하는 12개의 classification instruction과 19개의 non-classification instruction을 활용한 few-shot 방식으로 확인합니다.\"]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"Step 3. Instance Generation\"]}]},{t:\"p\",a:{},c:[\"앞선 과정을 통해 생성된 instruction과 task 유형이 주어지면 instruction-input-output 예시를 in-context 예시로 제공해줌으로써 instruction에 대한 instance를 독립적으로 생성합니다. 이때 분류 task 여부에 따라 다음과 같이 두 가지 접근 방법으로 나뉘게 됩니다.\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Input-first Approach\"]},\": 분류 task가 아닌 task. LLM에게 instruction을 입력으로 주고, 이에 해당하는 output을 출력하도록 만듭니다. 이때 다른 task에 대한 예시를 in-context 예시로 활용합니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Output-first Approach\"]},\": 분류 task. 주어진 instruction에 대해 가능한 클래스 레이블을 먼저 생성하고, 각 클래스 레이블에 맞춰서 적절한 input을 생성합니다.\"]}]}]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"Step 4. 필터링 및 후처리\"]}]},{t:\"p\",a:{},c:[\"생성된 데이터의 품질을 보장하기 위해 다음과 같은 여러 필터링 단계를 거칩니다:\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"유사도 검사\"]},\": 새로 생성된 지시사항이 기존 지시사항과 ROUGE-L 유사도가 0.7 미만인 경우에만 추가합니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"키워드 필터링\"]},\": 'image', 'picture', 'graph' 등 LLM이 처리할 수 없는 키워드가 포함된 지시사항을 제외합니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"중복 제거\"]},\": 완전히 동일하거나 같은 입력에 다른 출력을 가진 인스턴스를 필터링합니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"휴리스틱 기반 필터링\"]},\": 지시사항이 너무 길거나 짧은 경우, 출력이 입력의 단순 반복인 경우 등을 제거합니다.\"]}]}]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"Step 5. 반복\"]}]},{t:\"p\",a:{},c:[\"Step 1 ~ 4를 반복하여 최종적으로 52,000개 이상의 instruction과 82,000개 이상의 instance를 생성하였습니다.\"]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"[SELF-INSTRUCT DATASET: 다양성 및 퀄리티 높은 데이터셋]\"]}]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"Statistics\"]}]},{t:\"p\",a:{},c:[\"Self-Instruct를 통해 생성된 데이터셋에 대한 통계는 다음의 표와 같습니다. 약 52,000개의 instruction과 82,000개의 instruction으로 구성되어 있습니다.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/8wIQIdzrVKERZaSl2KsMa4LRM.png?width=4404&height=1200\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"Diversity\"]}]},{t:\"p\",a:{},c:[\"생성된 데이터셋의 다양성을 확인하기 위해 Berkely Neural Parser를 사용한 동사-명사 구조를 확인하였고, ROUGE-L 점수 오버랩을 통해 데이터 간의 중복도를 확인하였습니다.\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"동사-명사 구조 확인\"]}]},{t:\"p\",a:{},c:[\"다음과 같이 다양한 instruction format을 보여주면서 데이터셋의 다양성을 확인할 수 있습니다.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/YubfowFfn7mkPeTtQApXlx7YUk.png?width=4404&height=2158\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"ROUGE-L 점수 오버랩\"]}]},{t:\"p\",a:{},c:[\"ROUGE-L 오버랩이 그리 높지 않은 것으로 보아 instruction의 중복도가 낮다는 것을 확인할 수 있습니다.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/F7rNixugm4jMdlT5Ics41zXK3YY.png?width=4404&height=1063\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]}]}]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"Quality\"]}]},{t:\"p\",a:{},c:[\"생성된 데이터셋의 퀄리티 확인을 위해 200개의 instruction 및 instance 쌍을 랜덤하게 샘플링하여 전문가에게 instruction과 instance가 알맞은지 물어보는 방식으로 평가를 진행하였습니다. 그 결과 대부분의 instruction이 유의미하였으나, instance의 경우에 약간의 노이즈를 포함하고 있었습니다.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/OaZFTpHwa3Ct8hkT9FAHk8owG0.png?width=4404&height=1439\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"[SELF-INSTRUCT EXPERIMENTS]\"]}]},{t:\"p\",a:{},c:[\"Self-Instruct로 생성된 데이터셋을 활용한 모델 학습 관련 실험 결과는 다음과 같습니다.\"]},{t:\"ol\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"SUPERNI 벤치마크 테스트\"]},\": SELF-INSTRUCT로 파인튜닝된 GPT3(GPT3SELF-INST)는 기존 오리지널 GPT3보다 33% 더 높은 성능을 보였습니다. 이는 인간이 주석을 단 데이터로 훈련된 InstructGPT001과 거의 대등한 수준입니다.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/O71IGXu9ysVS0j8MhBxn9oHGmkQ.png?width=4404&height=1379\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"새로운 태스크에 대한 일반화 능력\"]},\": 연구팀은 252개의 새로운 사용자 지향 지시사항을 만들어 평가했습니다. GPT3_SELF-INST는 이 테스트에서도 우수한 성능을 보였으며, 공개된 다른 지시 데이터셋으로 훈련된 모델들을 크게 앞섰습니다.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/CHXYMzCs3Uo0GWr28CHMthHU.png?width=4404&height=1475\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"데이터 크기 및 품질의 중요성\"]},\": 더 많은 생성 데이터로 훈련할수록 모델의 성능이 향상되었지만, 약 16,000개의 지시사항 이후로는 성능 향상이 둔화되었습니다. 또한 연구팀은 InstructGPT003을 사용하여 생성된 데이터의 품질을 개선하는 실험도 진행했습니다. 이렇게 개선된 데이터로 훈련한 모델은 원본 데이터로 훈련한 모델보다 10% 더 높은 성능을 보였습니다.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/DskTMnx6K7v8JdMwF455IXt9M.png?width=4404&height=1677\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]}]}]},{t:\"h2\",a:{id:\"section-4\"},c:[{t:\"strong\",a:{},c:[\"SELF-INSTRUCT Conclusion\"]}]},{t:\"p\",a:{},c:[\"SELF-INSTRUCT는 인간의 노력을 최소화하면서 LLM을 활용하여 효율적으로 대규모의 다양한 instruction 데이터를 생성할 수 있는 방법을 제시하였습니다. 이를 통해 다양한 task에 대해 instruction 데이터셋을 제작하고, 이 데이터셋으로 모델을 학습하여 해당 task에 대한 모델의 instruction-following 능력을 크게 개선시켰습니다.\"]},{t:\"p\",a:{},c:[\"SELF-INSTRUCT는 다음과 같은 방법으로 금융 도메인에 적용될 수 있습니다:\"]},{t:\"ol\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:['금융 특화 지시사항 생성: 금융 도메인의 전문 지식을 바탕으로 초기 시드 태스크를 신중히 선별합니다. 예를 들어, \"주어진 재무제표를 분석하여 회사의 유동성 비율을 계산하라\" 또는 \"특정 주식의 과거 데이터를 바탕으로 향후 주가 동향을 예측하라\" 등의 태스크를 포함할 수 있습니다.']}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"금융 특화 언어모델 활용: 일반적인 LLM 대신 금융 도메인 텍스트로 사전학습된 언어모델을 사용하여 더욱 정확하고 전문적인 데이터를 생성합니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"금융 규제 및 컴플라이언스 반영: 데이터 생성 과정에서 금융 규제와 컴플라이언스 요구사항을 반영합니다. 예를 들어, 자금세탁방지(AML) 관련 시나리오를 다양하게 생성할 수 있습니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"시장 변동성 시뮬레이션: 다양한 시장 상황(강세장, 약세장, 변동성 높은 시기 등)을 반영한 데이터를 생성하여 모델의 robustness를 향상시킵니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"새로운 금융 상품 시나리오: 아직 시장에 출시되지 않은 새로운 금융 상품에 대한 가상의 데이터를 생성하여, 미래 시나리오에 대비할 수 있습니다.\"]}]}]},{t:\"p\",a:{},c:[\"이러한 방식은 기존에 희소했던 유형의 데이터를 손쉽게 생성함으로써 이점을 얻을 수 있습니다. 예를 들어, 신용평가 모델 개발 시 다양한 재무 상황과 신용 이력을 가진 가상의 고객 프로필을 대량으로 생성하여 모델은 희소한 케이스에 대해서도 학습할 수 있게 되어, 더욱 공정하고 정확한 신용평가가 가능해집니다. 이를 포함해 다양한 도메인에서 합성 데이터를 생성함으로써 모델을 다양한 관점에서 학습시킬 수 있고, 이는 금융 AI 모델에 큰 발전을 가져다 줄 것입니다.\"]},{t:\"p\",a:{},c:[\"결론적으로, SELF-INSTRUCT와 같은 혁신적인 방법론을 금융 도메인에 적용함으로써, 데이터 부족 문제를 극복하고 더욱 강력하고 유연한 AI 모델을 개발할 수 있습니다. 이는 단순히 기술적 진보를 넘어, 더 정확한 리스크 관리, 개인화된 금융 서비스, 효율적인 자산 관리 등을 통해 금융 산업 전반에 긍정적인 효과를 가져올 수 있습니다.\"]}],toc:[{id:\"section-1\",text:\"금융 AI 모델 개발의 초석, 데이터\"},{id:\"section-2\",text:\"금융 도메인 언어 모델 학습을 위한 합성데이터 생성 기법\"},{id:\"section-3\",text:\"SELF-INSTRUCT\"},{id:\"section-4\",text:\"SELF-INSTRUCT Conclusion\"}],images:[\"https://framerusercontent.com/images/kKq1iXY0Q8NMeCmsLawJ86NoUA.png?width=4404&height=1379\",\"https://framerusercontent.com/images/8wIQIdzrVKERZaSl2KsMa4LRM.png?width=4404&height=1200\",\"https://framerusercontent.com/images/YubfowFfn7mkPeTtQApXlx7YUk.png?width=4404&height=2158\",\"https://framerusercontent.com/images/F7rNixugm4jMdlT5Ics41zXK3YY.png?width=4404&height=1063\",\"https://framerusercontent.com/images/OaZFTpHwa3Ct8hkT9FAHk8owG0.png?width=4404&height=1439\",\"https://framerusercontent.com/images/O71IGXu9ysVS0j8MhBxn9oHGmkQ.png?width=4404&height=1379\",\"https://framerusercontent.com/images/CHXYMzCs3Uo0GWr28CHMthHU.png?width=4404&height=1475\",\"https://framerusercontent.com/images/DskTMnx6K7v8JdMwF455IXt9M.png?width=4404&height=1677\"],readMins:11},{id:21,page:\"Journal 22\",slug:\"synthetic-data-wizardlm\",kind:\"research\",topic:\"Data\",title:\"금융 도메인 언어모델 학습을 위한 합성데이터 생성 기법 Part2: Wizard LM\",originalTitle:\"금융 도메인 언어모델 학습을 위한 합성데이터 생성 기법 Part2: Wizard LM\",summary:\"Wizard LM 접근으로 확장하는 금융 학습 데이터\",language:\"ko\",date:\"Sep 17, 2024\",dateISO:\"2024-09-17\",source:\"https://onelineai.com/en/blog-research/synthetic-data-part2-wizardlm\",path:\"/research/synthetic-data-wizardlm\",nodes:[{t:\"blockquote\",a:{},c:[{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"이 글은 \",{t:\"a\",a:{href:\"https://arxiv.org/abs/2304.12244\"},c:[{t:\"strong\",a:{},c:[\"'WizardLM: Empowering Large Language Models to Follow Complex Instructions(2023)' \"]}]},\"논문을 기반으로 언어모델 학습을 위한 합성데이터 생성 기법을 설명하고 있습니다.\"]}]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"해당 기법으로 원라인에이아이가 생성한 데이터들은 \",{t:\"strong\",a:{},c:[\"허깅페이스 \"]},{t:\"a\",a:{href:\"https://huggingface.co/HAERAE-HUB\"},c:[{t:\"strong\",a:{},c:[\"HAE-RAE Hub\"]}]},\"에서 확인하실 수 있습니다.\"]}]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"특정 도메인 학습데이터는 \",{t:\"a\",a:{href:\"https://www.onelineai.com/contact\"},c:[{t:\"strong\",a:{},c:[\"원라인에이아이 AI팀\"]}]},\"에게 문의주시기 바랍니다.\"]}]}]}]},{t:\"p\",a:{},c:[\" \"]},{t:\"h2\",a:{id:\"section-1\"},c:[{t:\"strong\",a:{},c:[\"금융 도메인 언어 모델 학습을 위한 합성데이터 생성 기법\"]}]},{t:\"p\",a:{},c:[\"본 시리즈는 금융 분야에서 활용할 수 있는 인공지능 언어 모델의 학습을 유명 합성데이터 생성 기법을 다룰 예정입니다. 합성데이터 생성 기술 3가지를 살펴보면서 합성데이터의 중요성, 다양한 생성 기법, 그리고 이를 통한 언어 모델의 학습 효과 등을 세부적으로 탐구할 것입니다. 각 포스팅은 이론적 배경부터 실제 적용 사례까지 폭넓게 다루어, 본 시리즈를 통해 금융 분야의 AI 전문가들과 관심 있는 독자들에게 유용한 인사이트를 전달하고자 합니다.\"]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"Timeline\"]}]},{t:\"ol\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"SELF-INSTRUCT: Aligning Language Models with Self-Generated Instructions (\",{t:\"a\",a:{href:\"https://www.onelineai.com/blog/synthetic-data-part1-self-instruct\"},c:[\"Link\"]},\")\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"WizardLM: Empowering Large Language Models to Follow Complex Instructions (현 포스팅)\"]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"Cosmopedia: how to create large-scale synthetic data for pre-training (\",{t:\"a\",a:{href:\"https://www.onelineai.com/blog/synthetic-data-part3-cosmopedia\"},c:[\"Link\"]},\")\"]}]}]},{t:\"h2\",a:{id:\"section-2\"},c:[{t:\"strong\",a:{},c:[\"AI가 AI를 가르치는 시대의 학습 데이터 생성 방법\"]}]},{t:\"p\",a:{},c:['저번 포스팅에서는 Alpaca의 학습 데이터 생성에 사용되었던 ‘SELF-INSTRUCT’에 대해 알아봤습니다. 사람의 노력 없이 AI 모델이 자동적으로 생성한다는 점에서 굉장히 인상적이었는데, 본 포스팅에서 소개하고자 하는 \"WizardLM: Empowering Large Language Models to Follow Complex Instructions\"은 이러한 합성 데이터 생성 방식에 ‘Evol-Instruct’라는 아이디어를 접목하여 보다 복잡하고 난이도 있는 합성 데이터 생성 방식을 제시하였습니다.']},{t:\"h2\",a:{id:\"section-3\"},c:[{t:\"strong\",a:{},c:[\"WizardLM - Evol-Instruct\"]}]},{t:\"p\",a:{},c:[\"이 논문의 핵심 아이디어인 Evol-Instruct는 간단한 초기 instruction에서 시작해, 언어 모델 스스로 이를 점진적으로 복잡하고 다양한 형태로 진화시키는 방식입니다. 이는 크게 두 가지 방향으로 진행됩니다:\"]},{t:\"ol\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"In-depth Evolving\"]},\" (심화 발전): 기존 instruction을 더 복잡하고 구체적으로 만듭니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"In-breadth Evolving\"]},\" (폭 넓은 발전): 완전히 새로우면서도 동등한 복잡성을 가진 instruction을 생성합니다.\"]}]}]},{t:\"p\",a:{},c:[\"이 과정을 통해 WizardLM은 인간이 만든 지시사항보다 더 다양하고 복잡한 지시사항을 생성할 수 있게 되었습니다. 논문에서는 이러한 방식으로 생성된 지시사항들이 인간 평가에서 우수한 성능을 보였다고 합니다. 특히 주목할 만한 점은, 고난도 지시사항에 대해서는 WizardLM이 ChatGPT보다 더 선호되는 결과를 보였다는 것입니다.\"]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"[Evol-Instruct: AI가 스스로 데이터셋을 진화시키는 방법]\"]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/aogauLDneKNclzJsD7P3DteTpd0.png?width=4404&height=1851\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"WizardLM의 핵심 방법론인 Evol-Instruct는 초기 instruction에 대해 대규모 언어 모델(LLM)을 활용하여 자체적으로 더욱 복잡하고 다양한 형태로 변화시키는 방법입니다. 이 과정을 통해 다양한 태스크에 대해서 다양한 복잡도를 가지는 대규모 데이터셋을 자동으로 구축할 수 있습니다. 앞서 살펴본 ㄸEvol-Instruct의 전반적인 프로세스에 대해 자세하게 알아보겠습니다.\"]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"Step 1. Instruction Evolving\"]}]},{t:\"p\",a:{},c:[\"초기 instruction에 대해 난이도를 개선시키고 풍부함과 다양성을 확장시키는 방식으로 instruction에 변화를 줍니다. 이 과정에서 instruction evolver가 사용되는데, 이때 in-depth evolving과 in-breadth evolving의 2가지 유형으로 evolving이 진행됩니다.\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"In-depth Evolving\"]},\": instruction의 난이도를 높이는데 초점을 두고 있습니다. 제약 조건 추가, 심층화, 구체화, 추론 단계 늘리기, 입력 복잡화를 포함한 5가지의 evolving 방식을 사용합니다. 이때 언어 모델이 합리적이고 AI에 의해 쉽게 생각될 수 없는 instruction을 생성하도록 요구합니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"In-breadth Evolving\"]},\": instruction의 다양성을 높이는데 초점을 두고 있습니다. 주제 범위, 스킬 범위, 데이터셋 다양성 증진을 목표로 기존의 instruction에 기반하여 완전히 새로운 instruction을 생성하도록 유도합니다.\"]}]}]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"Step 2. Response Generation\"]}]},{t:\"p\",a:{},c:[\"‘instruction evolving’에서 사용된 모델과 똑같은 모델을 사용하여 evolved instruction에 대한 응답을 생성합니다.\"]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"Step 3. Elimination Evolving\"]}]},{t:\"p\",a:{},c:[\"‘instruction evolving’을 통해 생성된 instruction은 오류를 포함하고 있을 수도 있기 때문에 elimination evolving 과정을 통해 오류가 포함된 instruction은 배제하는 과정을 거쳐야 합니다. 다음의 4가지 evolution 실패 상황을 따라서 evolution 실패를 분류하였습니다.\"]},{t:\"ol\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"evolved instruction이 기존의 instruction과 비교하여 어떠한 새로운 정보도 제공하지 않음\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"evolved instruction이 LLM의 응답을 생성하는데 어렵게 만듦. LLM이 “sorry” 등의 거절 의사 또는 비교적 짧은 길이로 대답할 경우 LLM의 응답 생성에 어려움이 있다고 판단.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"LLM에 의해 생성된 응답이 오직 구두점과 불용어만을 포함하고 있음\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"evolved instruction이 evolution prompt에 등장하는 몇 단어를 카피함\"]}]}]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"Step 4. Repeat\"]}]},{t:\"p\",a:{},c:[\"Step 1 ~ 3을 반복하여 총 4번의 반복적인 evolution을 통해 다양한 종류와 난이도를 가지는 250,000개의 Evol-Instruct 데이터셋을 제작합니다. 이때 언어 모델로는 ChatGPT가 사용되었습니다.\"]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"[Experiments]\"]}]},{t:\"p\",a:{},c:[\"Evol-Instruct는 instruction 데이터셋 뿐만 아니라 모델을 평가하기 위한 Evol-Instruct 테스트셋도 함께 제작하여 여러 베이스라인 모델과 Evol-Instruct로 학습된 모델 간의 성능 비교를 진행하였습니다. Evol-Instruct 테스트셋은 다음 그림 와 같이 다양한 skill과 난이도를 가지는 테스트 데이터로 구성되어 있습니다. 이는 Evol-Instruct 테스트셋이 기존의 Alpaca와 Vicuna에 비교해서 더 고른 난이도 분포를 가진다는 것을 시사합니다.\"]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"(a) Evol-Instruct 테스트셋의 스킬 분포 (b) Vicuna, Alpaca, Evol-Instruct 테스트셋 간의 어려움 및 복잡도 레벨 비교\"]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/13JSgT1uPfP2AUQmMiMEwGIE2M.png?width=4404&height=1384\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"Evol-Instruct와 Vicuna 테스트셋에 대해 사람 평가와 GPT-4 평가를 진행한 결과 두 테스트셋 모두에서 Alpaca, Vicuna보다 더욱 개선된 성능을 보여주었습니다. 이는 Evol-Instruct로 생성된 데이터셋이 Alpaca와 Vicuna 학습 데이터보다 더 뛰어난 퀄리티를 가진다는 것을 시사합니다.\"]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Human Evaluation 결과\"]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/HTNi7rm8mrTlocAV7HSMOSdGqfc.png?width=4404&height=806\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"GPT-4 Evaluation 결과\"]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/umA0FVxd4RRLbnT2tWSfruWic.png?width=4404&height=999\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"또한 눈여겨볼 결과는 GPT-4를 이용한 자동 평가에서 WizardLM이 29개의 기술 중 17개 부문에서 ChatGPT 능력의 90% 이상을 달성했다는 점입니다. 이는 AI 모델을 활용한 Evol-Instruct로 생성된 데이터가 인간이 만든 지시사항과 근접한 성능을 낼 수 있음을 시사합니다.\"]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Evol-Instruct 테스트셋에서 WizardLM과 ChatGPT 사이의 각 스킬에 대한 GPT-4 Score\"]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/iZqHKSUGLVYDrBpvTS1omeflwM.png?width=4404&height=1485\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"Evol-Instruct를 통해 생성된 데이터셋에 대해 전반적인 분석을 보면 사람이 작성한 instruction인 ShareGPT와 AI 모델이 자동으로 생성한 Alpaca 데이터셋에 비해서 더 복잡하고 난이도 있는 instruction으로 구성되어 있는 것을 확인할 수 있습니다. 추가적으로 iteration이 늘어날수록 instruction의 복잡도가 조금씩 우상향한다는 것도 확인할 수 있습니다. 이는 WizardLM이 다른 방법들에 비해 뛰어난 성능을 보여주는 이유 중 하나입니다.\"]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"ShareGPT, Alpaca, Evol-Instruct의 instruction 간에 복잡도 비교 결과\"]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/nIqrym5dwiX4xS0Rpczim8QAa0.png?width=2260&height=800\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"h2\",a:{id:\"section-4\"},c:[{t:\"strong\",a:{},c:[\"Conclusion\"]}]},{t:\"p\",a:{},c:[\"간단한 instruction에서 시작해서 instruction을 진화시키는 Evol-Instruct를 다음과 같이 금융 합성 데이터셋 생성에 활용할 수 있습니다:\"]},{t:\"ol\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"금융 용어 및 개념 진화\"]},': \"주식이란 무엇인가?\"라는 간단한 질문에서 시작해, \"테크 기업의 주가가 실적 발표 이후 급락했을 때, 해당 기업의 장기 성장 잠재력과 현재 시장 상황을 고려하여 투자 전략을 수립하시오.\"와 같은 복잡한 태스크로 진화시킬 수 있습니다.']}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"시나리오 기반 데이터 생성\"]},': \"미국 연준이 기준금리를 0.25% 인상했다\"라는 기본적인 시장 상황에서 시작해, \"미국 연준의 0.25% 금리 인상, 중국의 경기 부양 정책 발표, 그리고 유럽의 에너지 위기가 동시에 발생했을 때, 아시아 신흥국 통화와 글로벌 원자재 시장에 미칠 영향을 분석하시오.\"와 같은 복잡한 시나리오로 발전시킬 수 있습니다.']}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"규제 준수 학습\"]},': \"자금세탁방지법의 기본 원칙은 무엇인가?\"라는 간단한 규제 설명에서 시작해, \"글로벌 금융 기관이 미국, EU, 그리고 아시아 시장에서 동시에 운영될 때, 각 지역의 자금세탁방지법과 개인정보보호법을 모두 준수하면서 효율적인 고객 실사(Due Diligence) 프로세스를 설계하시오.\"와 같은 복잡한 규제 시나리오로 확장할 수 있습니다.']}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"리스크 분석 고도화\"]},': \"시장 리스크란 무엇인가?\"라는 기본적인 리스크 정의에서 시작해, \"글로벌 투자 은행이 신흥국 채권, 선진국 주식, 그리고 암호화폐 파생상품에 투자할 때, 지정학적 리스크, 규제 리스크, 시장 리스크, 그리고 운영 리스크를 종합적으로 고려한 리스크 평가 모델을 개발하고, 스트레스 테스트 시나리오를 제시하시오.\"와 같은 복합적 리스크 분석으로 진화시킬 수 있습니다.']}]}]},{t:\"p\",a:{},c:[\"결론적으로, Evol-Instruct 방식의 합성 데이터 생성은 기존의 간단한 금융 데이터를 추가적으로 발전시켜서 금융 AI의 능력을 한 단계 높일 수 있습니다. 이는 단순히 데이터 부족 문제를 해결하는 것을 넘어, AI의 창의성과 문제 해결 능력을 극대화시킬 수 있는 방법입니다. 우리는 이를 통해 금융 전문가들에게 더욱 신뢰할 수 있고, 정교한 AI 파트너를 제공할 수 있게 될 것입니다.\"]},{t:\"p\",a:{},c:[\"고성능 AI 모델의 학습을 위해서는 데이터의 양 뿐만 아니라 데이터의 질 및 다양성이 대두되는만큼, 금융 AI 모델의 미래 또한 데이터의 질과 다양성을 고려해야 합니다. 그리고 여기에 Evol-Instruct를 활용한다면 금융 AI의 새로운 장을 열어갈 수 있을 것입니다.\"]}],toc:[{id:\"section-1\",text:\"금융 도메인 언어 모델 학습을 위한 합성데이터 생성 기법\"},{id:\"section-2\",text:\"AI가 AI를 가르치는 시대의 학습 데이터 생성 방법\"},{id:\"section-3\",text:\"WizardLM - Evol-Instruct\"},{id:\"section-4\",text:\"Conclusion\"}],images:[\"https://framerusercontent.com/images/aogauLDneKNclzJsD7P3DteTpd0.png?width=4404&height=1851\",\"https://framerusercontent.com/images/13JSgT1uPfP2AUQmMiMEwGIE2M.png?width=4404&height=1384\",\"https://framerusercontent.com/images/HTNi7rm8mrTlocAV7HSMOSdGqfc.png?width=4404&height=806\",\"https://framerusercontent.com/images/umA0FVxd4RRLbnT2tWSfruWic.png?width=4404&height=999\",\"https://framerusercontent.com/images/iZqHKSUGLVYDrBpvTS1omeflwM.png?width=4404&height=1485\",\"https://framerusercontent.com/images/nIqrym5dwiX4xS0Rpczim8QAa0.png?width=2260&height=800\"],readMins:9},{id:22,page:\"Journal 23\",slug:\"synthetic-data-cosmopedia\",kind:\"research\",topic:\"Data\",title:\"금융 도메인 언어모델 학습을 위한 합성데이터 생성 기법 Part3: Cosmopedia\",originalTitle:\"금융 도메인 언어모델 학습을 위한 합성데이터 생성 기법 Part3: Cosmopedia\",summary:\"Cosmopedia 접근을 활용한 도메인 학습 데이터 구성\",language:\"ko\",date:\"Sep 18, 2024\",dateISO:\"2024-09-18\",source:\"https://onelineai.com/en/blog-research/synthetic-data-part3-cosmopedia\",path:\"/research/synthetic-data-cosmopedia\",nodes:[{t:\"blockquote\",a:{},c:[{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"이 글은 Hugging Face Team의 \",{t:\"a\",a:{href:\"https://huggingface.co/blog/cosmopedia\"},c:[{t:\"strong\",a:{},c:[\"'Cosmopedia: how to create large-scale synthetic data for pre-training'\"]}]},\" 블로그 글 기반으로 언어모델 학습을 위한 합성데이터 생성 기법을 설명하고 있습니다.\"]}]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"해당 기법으로 원라인에이아이가 생성한 데이터들은 \",{t:\"strong\",a:{},c:[\"허깅페이스 \"]},{t:\"a\",a:{href:\"https://huggingface.co/HAERAE-HUB\"},c:[{t:\"strong\",a:{},c:[\"HAE-RAE Hub\"]}]},\"에서 확인하실 수 있습니다.\"]}]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"특정 도메인 학습데이터는 \",{t:\"a\",a:{href:\"https://www.onelineai.com/contact\"},c:[{t:\"strong\",a:{},c:[\"원라인에이아이 AI팀\"]}]},\"에게 문의주시기 바랍니다.\"]}]}]}]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-1\"},c:[{t:\"strong\",a:{},c:[\"금융 도메인 언어 모델 학습을 위한 합성데이터 생성 기법\"]}]},{t:\"p\",a:{},c:[\"본 시리즈는 금융 분야에서 활용할 수 있는 인공지능 언어 모델의 학습을 유명 합성데이터 생성 기법을 다룹니다. 합성데이터 생성 기술 3가지를 살펴보면서 합성데이터의 중요성, 다양한 생성 기법, 그리고 이를 통한 언어 모델의 학습 효과 등을 세부적으로 탐구할 것입니다. 각 포스팅은 이론적 배경부터 실제 적용 사례까지 폭넓게 다루어, 본 시리즈를 통해 금융 분야의 AI 전문가들과 관심 있는 독자들에게 유용한 인사이트를 전달하고자 합니다.\"]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"Timeline\"]}]},{t:\"ol\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"SELF-INSTRUCT: Aligning Language Models with Self-Generated Instructions (\\b\",{t:\"a\",a:{href:\"https://www.onelineai.com/blog/synthetic-data-part1-self-instruct\"},c:[\"Link\"]},\")\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"WizardLM: Empowering Large Language Models to Follow Complex Instructions (\",{t:\"a\",a:{href:\"https://www.onelineai.com/blog/synthetic-data-part2-wizardlm\"},c:[\"Link\"]},\")\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Cosmopedia: how to create large-scale synthetic data for pre-training (현 포스팅)\"]}]}]}]},{t:\"h2\",a:{id:\"section-2\"},c:[{t:\"strong\",a:{},c:[\"끊임 없이 변화하는 세상에서의 LLM 학습\"]}]},{t:\"p\",a:{},c:[\"앞선 포스팅에서 SELF-INSTRUCT와 Evol-Instruct로 사람의 도움 없이 AI 모델이 자동적으로 instruction data를 생성하고 스스로 발전시킬 수 있다는 것을 확인하였습니다. 하지만 이 방법론의 포커스는 fine-tuning data를 생성하는데 맞춰져 있기 때문에 pre-training data를 제작하는데는 사용될 수 없습니다. 그렇다면 pre-training data를 제작하기 위해서는 어떤 방법이 사용될 수 있을까? Huggingface Team에서 공개한 ‘Cosmopedia’는 pre-training data 제작에 대한 해결책을 제안해줍니다.\"]},{t:\"h2\",a:{id:\"section-3\"},c:[{t:\"strong\",a:{},c:[\"Cosmopedia\"]}]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"[Cosmopedia: pre-training을 위한 대규모 합성 데이터셋]\"]}]},{t:\"p\",a:{},c:[\"Cosmopedia는 LLM을 사전 훈련하기 위해 데이터셋의 사이즈를 수천 개에서 수백만 개의 샘플로 확장하는 데 중점을 둔 프로젝트입니다. 이는 기존의 합성 instruction-tuning 데이터셋 생성 방법과는 달리, 대규모 사전 훈련 데이터셋 생성에 따른 고유한 도전 과제들을 다루고 있습니다.\"]},{t:\"p\",a:{},c:[\"Cosmopedia는 합성 데이터로 학습되어 좋은 성능을 보여주었던 Phi 시리즈에 영감을 받아서 오픈소스 언어 모델을 활용하여 다양한 합성 교과서, 블로그 포스트, 글, 기사 등의 텍스트 데이터셋을 생성하였습니다.\"]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"[Cosmopedia Method]\"]}]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"Prompts curation\"]}]},{t:\"p\",a:{},c:[\"Phi-1.5 리포트에서 20,000개의 토픽에 대해 약 200억 개 토큰 분량의 합성 데이터셋을 제작하였던 것처럼, 연구진들은 3천만 개 이상의 파일과 약 250억 토큰 분량의 합성 데이터셋을 제작하기 위해 다음의 방법을 결합하여 사용하였습니다.\"]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"(왼쪽) Cosmopedia prompt 생성을 위한 데이터 소스 분포 (오른쪽) Curated Source 내의 데이터셋 분포\"]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/Cyi8lKWaUc2sVjnZbkRNri2QvQ.png?width=1712&height=736\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Curated Sources\"]}]},{t:\"p\",a:{},c:[\"Stanford, Khan Academy, OpenStax, WikiHow와 같은 신뢰할 수 있는 교육 소스에서 추출한 주제를 활용하여 LLM이 학습할 가치 있는 다양한 주제를 다룹니다. 여기에 다양성을 추가하기 위해 대상 청중과 스타일의 다양성을 활용하는 전략을 채택하여 단일 주제를 여러 번 재사용하되, 대상 청중(예: 어린이 vs 대학생)과 생성 스타일(예: 학술 교과서 vs 블로그 포스트)을 변경하는 방식을 채용하였습니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Web Data\"]}]},{t:\"p\",a:{},c:[\"웹 데이터를 활용하여 대규모 프롬프트를 생성하는 방법입니다. 이 방법은 RefinedWeb과 같은 대규모 웹 데이터셋을 145개의 클러스터로 분류하고, Mixtral 모델을 사용하여 각 클러스터의 주제를 식별한 후, 교육적 가치가 있는 112개의 주제만을 선별하여 사용합니다. 최종적으로, 선별된 웹 샘플을 기반으로 교과서 스타일의 텍스트를 생성하도록 AI 모델에 지시하여 2300만 개의 프롬프트를 구축했으며, 이는 전체 Cosmopedia 프롬프트의 80% 이상을 차지하고 있습니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Instruction datasets and stories\"]}]},{t:\"p\",a:{},c:[\"모델의 상식과 기초 지식 향상을 위해 다양한 instruction 데이터셋을 기반으로 일상 지식과 기본 상식을 포함한 이야기를 생성합니다. 이 과정에서 스토리텔링에 부적합한 카테고리는 제외되었으며, 결과적으로 생성된 Cosmopedia의 클러스터 분포는 웹 데이터의 클러스터와 유사한 양상을 보여, 다양성과 포괄성을 달성했음을 시사합니다.\"]}]}]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Cosmopedia 클러스터\"]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/9P2QHWX8QM7vNAkGNNSS8JZjLM.png?width=2286&height=1464\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"[Cosmopedia Technical Stack]\"]}]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"Textbooks Generation\"]}]},{t:\"p\",a:{},c:[\"앞선 과정을 통해서 다양한 고품질의 소스 데이터가 수집되었습니다. Cosmopedia는 수집된 소스 데이터를 Mixtral-8x7B-Instruct-v0.1 모델을 활용하여 다수의 H100 GPU에서 약 10k GPU hours 동안 데이터를 재생성하였습니다.\"]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"Benchmark Decontamination\"]}]},{t:\"p\",a:{},c:[\"합성 데이터 생성만큼이나 중요한 것이 벤치마크 decontamination입니다. 이는 학습 데이터에 포함되어 있을 수 있을지도 모르는 벤치마크 데이터를 제거하는 작업을 의미합니다. Cosmopedia는 이러한 문제를 해결하기 위해 다음과 같은 방법을 활용하였습니다.\"]},{t:\"ol\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"10개의 토큰이 오버랩되는 경우 decontamination 후보로 지정합니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"후보로 지정된 샘플 중에서 다음의 조건을 만족하는 샘플 제거. 조건: \",{t:\"em\",a:{},c:[\"(일치 문자열 길이)(벤치마크 샘플 길이) > 0.5\"]}]}]}]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"Training Stack\"]}]},{t:\"p\",a:{},c:[\"Cosmopedia 데이터셋을 이용하여 Llama2 구조의 1B LLM을 학습시킨 결과, 비슷한 사이즈의 TinyLlama 1.1B와 Qwen-1.5-1B와 비슷한 수준의 성능을 보여주는 것을 확인할 수 있습니다.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-4\"},c:[{t:\"strong\",a:{},c:[\"Cosmopedia Conclusion\"]}]},{t:\"p\",a:{},c:[\"Cosmopedia와 같은 대규모 합성 데이터셋 생성 방법론을 금융 도메인에 접목시키면, 다음과 같이 금융 도메인의 다양한 영역을 포괄하는 대규모 합성 데이터를 생성할 수 있습니다.\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"신뢰할 수 있는 금융 교육 자료 활용 데이터: 고품질의 금융 교육 자료를 바탕으로 고품질의 금융 교육 컨텐츠를 생성합니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"금융 웹 데이터 클러스터링: SEC 파일링, 금융 뉴스, 전문가 블로그 등의 웹 데이터를 클러스터링하여 다양한 금융 주제를 식별하고, 이를 바탕으로 데이터를 생성할 수 있습니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"금융 특화 지시 데이터셋: 투자 분석, 리스크 관리, 규제 준수 등 금융 전문가들의 일상적인 업무를 반영한 데이터셋을 구축할 수 있습니다.\"]}]}]},{t:\"p\",a:{},c:[\"Cosmopedia 방법론을 활용한 합성 데이터 생성 방식은 fine-tuning data를 생성하는 SELF-INSTRUCT와 Evol-Instruct와 달리 고품질 소스 데이터를 수집하여 pre-training 데이터로 재생성하였습니다. 이러한 방식으로 생성된 데이터셋은 모델이 금융 도메인에 대해 전반적으로 이해할 수 있도록 만드는 사전 학습 데이터가 될 수 있습니다.\"]},{t:\"p\",a:{},c:[\"하지만 언제나 그렇듯 합성 데이터를 제작할 때는 몇 가지 중요한 고려사항이 있습니다. 데이터 소스의 민감성과 규제 준수 문제, 데이터의 최신성 유지, 그리고 편향성 방지 등이 주요 과제가 될 것입니다. 또한, 생성된 합성 데이터의 품질과 신뢰성을 지속적으로 검증하고 개선하는 과정이 필요할 것입니다.\"]},{t:\"p\",a:{},c:[\"결론적으로, Cosmopedia 방식의 대규모 합성 데이터셋 생성 방법론을 금융 분야에 적용하는 것은 금융 AI의 성능과 적용 범위를 크게 확장시켜서 금융 서비스의 혁신, 리스크 관리 개선, 그리고 더 나은 금융 의사결정 지원 등 다양한 측면에서 긍정적인 영향을 미칠 것으로 기대됩니다. 이는 최종적으로 더욱 강력하고 신뢰할 수 있는 금융 AI 시스템을 구축할 수 있도록 만들 것입니다.\"]},{t:\"h2\",a:{id:\"section-5\"},c:[{t:\"strong\",a:{},c:[\"시리즈를 마치며..\"]}]},{t:\"p\",a:{},c:[\"금융 AI 언어 모델을 위한 합성 데이터셋 생성 방법에 대한 포스팅 시리즈를 마무리하며, 이 분야의 중요성과 잠재력을 다시 한번 확인했습니다. 금융 산업에서 AI의 역할이 점점 더 커지고 있는 만큼, 고품질의 학습 데이터 확보는 그 어느 때보다 중요해졌습니다. 이 시리즈를 통해 우리는 다양한 합성 데이터 생성 기법들을 살펴보았고, 각 방법의 장단점을 분석했습니다.\"]},{t:\"p\",a:{},c:[\"이러한 방법들은 단순히 데이터 부족 문제를 해결하는 것을 넘어, 금융 AI 모델의 성능과 신뢰성을 크게 향상시킬 수 있는 잠재력을 가지고 있습니다. 특히 개인정보 보호와 데이터 보안이 중요한 금융 분야에서, 합성 데이터의 활용은 혁신적인 해결책이 될 수 있습니다. 그러나 동시에 우리는 이러한 기술의 한계와 잠재적인 위험성에 대해서도 항상 주의를 기울여야 합니다. 생성된 데이터의 품질과 현실성을 지속적으로 검증해야 하며, 실제 데이터와의 균형을 잘 맞추는 것 또한 중요합니다.\"]},{t:\"p\",a:{},c:[\"우리 회사는 이러한 최신 연구를 바탕으로 금융 특화 합성 데이터 생성 파이프라인을 개발하였습니다. 이를 통해 고객사 여러분은 양질의 학습 데이터를 손쉽게 확보하고, 더욱 정교한 금융 AI 모델을 개발할 수 있을 것입니다. 금융의 미래를 함께 만들어갈 파트너를 찾고 계시다면, 주저하지 마시고 연락 주시기 바랍니다. 함께라면, 우리는 금융 AI의 새로운 지평을 열어갈 수 있을 것입니다.\"]}],toc:[{id:\"section-1\",text:\"금융 도메인 언어 모델 학습을 위한 합성데이터 생성 기법\"},{id:\"section-2\",text:\"끊임 없이 변화하는 세상에서의 LLM 학습\"},{id:\"section-3\",text:\"Cosmopedia\"},{id:\"section-4\",text:\"Cosmopedia Conclusion\"},{id:\"section-5\",text:\"시리즈를 마치며..\"}],images:[\"https://framerusercontent.com/images/Cyi8lKWaUc2sVjnZbkRNri2QvQ.png?width=1712&height=736\",\"https://framerusercontent.com/images/9P2QHWX8QM7vNAkGNNSS8JZjLM.png?width=2286&height=1464\"],readMins:8},{id:23,page:\"Journal 24\",slug:\"financial-reasoning\",kind:\"research\",topic:\"Finance\",title:\"대규모 언어 모델을 통한 금융 추론: 투자 의견 생성의 시대\",originalTitle:\"대규모 언어 모델을 통한 금융 추론: 투자 의견 생성의 시대\",summary:\"투자 의견 생성과 금융 추론에 대한 연구 관점\",language:\"ko\",date:\"Oct 10, 2024\",dateISO:\"2024-10-10\",source:\"https://onelineai.com/en/blog-research/%EB%8C%80%EA%B7%9C%EB%AA%A8-%EC%96%B8%EC%96%B4-%EB%AA%A8%EB%8D%B8%EC%9D%84-%ED%86%B5%ED%95%9C-%EA%B8%88%EC%9C%B5-%EC%B6%94%EB%A1%A0-%ED%88%AC%EC%9E%90-%EC%9D%98%EA%B2%AC-%EC%83%9D%EC%84%B1%EC%9D%98-%EC%8B%9C%EB%8C%80\",path:\"/research/financial-reasoning\",nodes:[{t:\"blockquote\",a:{},c:[{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"‘\",{t:\"a\",a:{href:\"https://arxiv.org/abs/2305.01505\"},c:[{t:\"strong\",a:{},c:[\"Beyond Classification: Financial Reasoning in State-of-the-Art Language Models\"]}]},{t:\"strong\",a:{},c:[\"’ \"]},\"논문을 기반으로 언어모델의 금융 도메인 추론 능력 분석에 관한 내용을 다루고 있습니다. 해당 논문은 \",{t:\"a\",a:{href:\"https://finnlp-muffin-ijcai23.github.io/\"},c:[{t:\"strong\",a:{},c:[\"FinNLP @ IJCAI2023\"]}]},\" 학회에 게재되었습니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"이 시리즈는 원라인에이아이 AI에서 발표한 논문을 리뷰합니다. 논문과 관련해서 궁금한 내용이 있다면 \",{t:\"a\",a:{href:\"https://www.onelineai.com/contact\"},c:[{t:\"strong\",a:{},c:[\"원라인에이아이 AI팀\"]}]},\"에게 문의주시기 바랍니다.\"]}]}]}]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-1\"},c:[{t:\"strong\",a:{},c:[\"LLM의 등장과 금융 추론에서의 활용\"]}]},{t:\"p\",a:{},c:[\"2022년 겨울, OpenAI의 ChatGPT 등장은 말 그대로 세계를 뒤흔들어 놓았습니다. 사람들은 ChatGPT의 엄청난 성능과 활용 가능성에 열광하기 시작했고, 이러한 열기는 Meta AI의 Llama를 포함한 여러 오픈소스 대규모 언어 모델(LLM)의 등장으로 이어졌습니다. 이로 인해 적은 자원으로도 누구나 LLM을 사용할 수 있게 되었습니다.\"]},{t:\"p\",a:{},c:['오픈소스 LLM의 등장으로 인해 LLM은 법률 및 의학 등 다양한 분야에 활용되기 시작했습니다. 그러나 이에 반해 금융 도메인에서의 활용은 상대적으로 미비했습니다. \"',{t:\"a\",a:{href:\"https://arxiv.org/abs/2305.01505\"},c:[\"Beyond Classification: Financial Reasoning in State-of-the-Art Language Models\"]},\"\\\" 논문은 이러한 문제점을 논의하면서, 최신 LLM이 금융 도메인에서 어떻게 활용될 수 있는지, 특히 투자 의사결정에서 중요한 '금융 추론'에 대한 가이드라인을 제시했습니다. 해당 논문은 금융 도메인에서의 LLM 활용 및 분석의 공로를 인정받아 FinNLP @ IJCAI2023 학회에 게재되었습니다.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-2\"},c:[{t:\"strong\",a:{},c:[\"Introduction\"]}]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"논문의 배경\"]}]},{t:\"p\",a:{},c:[\"기존의 금융 자연어 처리 연구는 주로 토큰 분류 또는 시퀀스 분류와 같은 단순한 작업에 집중되어 있고, 언어 모델을 활용한 복잡한 금융 추론 연구는 거의 이루어지지 않았습니다. 이에 따라 논문에서는 LLM이 금융 분야에서 논리적으로 일관된 투자 의견을 생성할 수 있는가에 대한 답을 찾고자 했습니다.\"]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"논문의 목표\"]}]},{t:\"p\",a:{},c:[\"논문에서는 금융 도메인에서 LLM의 복잡한 추론 작업 수행 능력을 탐구하기 위해, 투자 의견 생성(Financial Investment Opinion Generation, FIOG)이라는 새로운 태스크를 설정했습니다. 이 태스크는 모델이 투자 결정을 내리는 데 필요한 논리적이고 설득력 있는 의견을 생성하는 것이 핵심입니다.\"]},{t:\"p\",a:{},c:[\"FIOG를 활용하여 여러 GPT 계열 모델들에 대해 다양한 실험을 진행했고, 그 결과 모델 크기, In-Context Question Answering, Instruction-Tuning 등 다양한 요인이 LLM의 금융 추론 능력에 영향을 준다는 사실을 발견했습니다. 또한, 합성 금융 추론 데이터셋 \",{t:\"strong\",a:{},c:[\"sFIOG\"]},\"(Synthetic-Financial Investment Opinion Generation)와 해당 데이터셋의 생성 방법을 함께 제안했습니다.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-3\"},c:[{t:\"strong\",a:{},c:[\"FIOG(Financial Investment Opinion Generation)\"]}]},{t:\"p\",a:{},c:[\"논문에서 제안된 핵심 태스크인 금융 투자 의견 생성(FIOG) 태스크는 금융 관련 데이터를 활용하여 인공지능이 투자 의견을 생성하는 작업입니다. FIOG는 모델에게 문제 해결에 필요한 필수적인 정보를 입력으로 주는데, 이때 두 가지 주요 입력 유형이 있습니다: \",{t:\"strong\",a:{},c:[\"전체 텍스트 입력\"]},\" & \",{t:\"strong\",a:{},c:[\"질문-답변(Q&A) 입력\"]}]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"1) 전체 텍스트 입력\"]}]},{t:\"p\",a:{},c:[\"이 방식에서는 LLM에 완전한 텍스트 정보가 주어집니다. 예를 들어 투자 보고서의 요약이나 관련 자료들이 제공됩니다. 이로 인해 모델은 주어진 모든 정보를 바탕으로 투자 의견을 생성하게 됩니다. 간단히 말하면, 모든 배경 정보와 데이터를 포함한 상태에서 모델이 스스로 결론을 도출하도록 유도하는 것입니다.\"]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"2) 질문-답변(Q&A) 입력\"]}]},{t:\"p\",a:{},c:[\"Q&A 방식은 모델에 질문과 그에 대한 답변 쌍이 제공됩니다. 이는 모델이 구체적인 질문에 대한 답을 바탕으로 투자 의견을 생성하도록 유도하는 방식입니다. Q&A 방식은 모델이 불필요한 정보에 혼란을 겪지 않고, 꼭 필요한 데이터에만 집중할 수 있도록 도와줍니다. 즉, 정보를 더 체계적이고 통제된 방식으로 제공하는 셈입니다.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-4\"},c:[{t:\"strong\",a:{},c:[\"Dataset Creation\"]}]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"데이터의 다양성 보장\"]}]},{t:\"p\",a:{},c:[\"논문에서는 향후 금융 추론 연구를 위해 합성 금융 투자 의견 데이터셋인 Synthetic-Financial Investment Opinion Generation(sFIOG)과 해당 데이터셋의 제작 방식을 공개했습니다. 이 데이터셋의 질문은 전문가가 중요하다고 판단한 정보에 중점을 두어 만들어졌습니다. 이는 사람 중심의 질의응답을 바탕으로 보다 다양한 측면에서, 풍부한 금융 추론 능력을 개발하는 데 기여할 수 있도록 만들어졌습니다. sFIOG 데이터셋의 생성 프로세스는 다음과 같습니다:\"]},{t:\"ol\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"전문가가 작성한 애널리스트 보고서 수집\"]},\": J.P Morgan, Truist Financial Corp, Oppenheimer & Co. 같은 다양한 소스로부터 1,087개의 애널리스트 보고서를 수집. 이 보고서들은 미국 주식 시장의 752개 기업을 다루고 있음.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"전문가 작성 투자 논지 집합 구성\"]},\": 보고서에서 '투자 논지(Investment Thesis)'와 '관련 리스크' 섹션을 추출해 전문가 작성 투자 논지를 구성 → \",{t:\"strong\",a:{},c:[\"1,807개의 투자 논지\"]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Full-Text 타입 입력 구성\"]},\": 각 애널리스트 보고서의 초록을 수집해 Full-Text 형식의 입력 데이터를 제작 → \",{t:\"strong\",a:{},c:[\"4,836개의 투자 논지\"]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Q&A 타입 입력을 위한 질문 생성\"]},\": GPT-3.5-Turbo API를 이용해 Full-Text 데이터를 바탕으로 중요한 정보를 묻는 질문들을 생성 → \",{t:\"strong\",a:{},c:[\"11,802개의 투자 논지\"]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"더미 답변 생성\"]},\": 생성된 질문에 대해 더미 답변을 생성하고, 현실과 너무 동떨어진 답변을 제거하기 위해 인력 검수를 거침\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"투자 의견 생성\"]},\": GPT-3.5-Turbo API를 통해 Full-Text 및 Q&A 형식의 입력을 기반으로 투자 의견을 생성\"]}]}]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Dataset Overview. (RE)는 재생성된 데이터를 의미함.\"]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/EnU0yytARDc1IA6pIOhPzRTlaFs.png?width=1920&height=180\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"데이터 정량 평가 결과\"]}]},{t:\"p\",a:{},c:[\"질문 생성 방식의 성능을 평가하기 위해 어휘적 다양성(lexical diversity)과 구문적 다양성(syntactic diversity) 측면에서 두 가지 평가 지표인 \",{t:\"strong\",a:{},c:[\"Mass\"]},\"와 \",{t:\"strong\",a:{},c:[\"HD-D\"]},\"를 사용했습니다. 여기에 추가적으로 문장의 다양성을 평가하기 위해 \",{t:\"strong\",a:{},c:[\"Syntactic Sim\"]},\"이 사용되었습니다. 결과적으로, 제안된 질문 생성 방법은 어휘적, 구문적 다양성이 높은 데이터를 생성하는 것으로 나타났습니다.\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"HD-D\"]},\": 텍스트의 어휘적 다양성을 평가하는 지표. 특정 텍스트 내에서 단어들이 얼마나 고르게 분포되어 있는지, 다양한 단어들이 얼마나 사용되는지를 측정. 해당 metric의 score가 높을수록 어휘적 풍부성이 높다는 것을 의미.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Mass\"]},\": 텍스트의 정보 밀도를 평가하는 지표로, 주로 문장의 구조나 어휘의 선택을 분석하여 텍스트가 얼마나 정보량이 높은지, 혹은 불필요한 정보가 적은지를 판단. 정보가 많이 담겨 있으면 높은 점수를 받습니다. 해당 metric의 score가 높을수록 어휘적 풍부성이 낮다는 것을 의미.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Syntactic Sim\"]},\": 문법적인 구조가 얼마나 비슷한지 측정하는 지표. 두 개의 텍스트가 있을 때 그 텍스트들이 문법적으로 얼마나 유사한지 비교. 구조가 유사하면 높은 점수를 받음.\"]}]}]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"few-shot generation과 논문에서 제안된 방법(Step 4)의 퀄리티 평가 결과\"]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/4YOwRQ54gleLU1nfahIbKfNJMvs.png?width=4400&height=605\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"h2\",a:{id:\"section-5\"},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-6\"},c:[{t:\"strong\",a:{},c:[\"In-Context Question Answering\"]}]},{t:\"p\",a:{},c:[\"논문에서 제안된 \",{t:\"strong\",a:{},c:[\"In-Context Question Answering\"]},\"은 기존의 ‘In-Context Learning’ 방식에서 벗어나, 질문과 답변 쌍을 활용하여 더 정확한 맥락 기반 생성 결과를 얻는 방법입니다.\"]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"In-Context Learning의 한계\"]}]},{t:\"p\",a:{},c:[\"일반적으로 언어 모델들은 학습 과정에서 거대한 양의 데이터를 바탕으로 다양한 정보를 암기하게 되며, 이후 학습된 지식을 바탕으로 새로운 데이터를 처리합니다. 하지만 이 과정에서 현실과 일치하지 않거나, 시대에 뒤떨어진 정보를 생성하는 현상, 즉 \",{t:\"strong\",a:{},c:[\"hallucination\"]},\" 문제가 발생할 수 있습니다. 특히, 금융 분야처럼 시시각각 변화하는 정보가\"]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"In-Context Question Answering의 장점\"]}]},{t:\"p\",a:{},c:[\"논문에서는 이러한 문제를 해결하기 위해 \",{t:\"strong\",a:{},c:[\"In-Context Question Answering\"]},\" 방식을 제안했습니다. 이 방식은 기존의 전체 텍스트를 맥락으로 제공하는 대신, 모델이 학습할 때 질문과 답변 쌍을 입력으로 제공하여 더 명확하고 통제된 결과를 도출하도록 유도합니다. 실험 결과, 이 방식은 기존의 방법에 비해 다음과 같은 이점을 가지고 있다는 것이 확인되었습니다:\"]},{t:\"ol\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"의도치 않은 맥락 생성 비율 감소\"]},\": 예를 들어, 팬데믹 이후 시점에서의 투자 의견을 생성할 때, 기존 방식으로는 약 11.12%가 팬데믹 관련 내용을 불필요하게 포함한 반면, In-Context Question Answering 방식에서는 단 1.63%만이 부적절한 경향을 보였습니다. 이는 질문-답변 형식이 모델로 하여금 주어진 정보에 집중하게 만들고, 불필요한 텍스트로부터 주의를 분산시키지 않기 때문입니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"인간 평가자의 긍정적 선호도\"]},\": 1,000개의 샘플을 기반으로 한 실험에서, 인간 평가자들은 약 61.2%의 경우에 이 방식으로 생성된 의견이 투자에 가장 도움이 된다고 응답했으며, 48%는 이 방식이 논리적으로 가장 일관된 의견을 제시한다고 평가했습니다. 이는 기존의 텍스트 기반 학습 방식보다 더 높은 신뢰성과 정확성을 제공할 수 있음을 시사합니다.\"]}]}]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"수집된 투자 논지의 퀄리티 평가 결과: 초록색은 전문가 작성 데이터, 파란색은 full-text type, 곤색은 Q&A type. G1 & G2는 Q1 & Q2에 대한 GPT-4 답변. H1 & H2는 Q1 & Q2에 대한 사람 답변.\"]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/rPZtP0rRnpgPi5O5VK6hN9w8.png?width=4400&height=1168\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"p\",a:{},c:[\"In-Context Question Answering 방식은 기존의 학습 방식에 비해 더 높은 통제성과 정확성을 제공하며, 금융 분야와 같이 변화무쌍한 정보 환경에서 특히 유용한 것으로 나타났습니다. 이를 통해 대형 언어 모델이 보다 신뢰할 수 있는 금융 정보를 제공할 수 있는 가능성을 보여주었으며, 다양한 분야에서도 이 방식이 응용될 수 있는 잠재력을 가지고 있습니다.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-7\"},c:[{t:\"strong\",a:{},c:[\"Experiments\"]}]},{t:\"p\",a:{},c:[\"논문에서는 금융 도메인에서 LLM의 잠재력을 탐구하며, 금융 투자 의견 생성에서의 성능을 다각도로 평가했습니다. 특히, \",{t:\"strong\",a:{},c:[\"모델 크기\"]},\", \",{t:\"strong\",a:{},c:[\"Instruction-Tuning\"]},\", \",{t:\"strong\",a:{},c:[\"데이터셋 크기\"]},\"에 따른 금융 추론 능력을 집중적으로 분석하였습니다.\"]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"Experimental Setup\"]}]},{t:\"p\",a:{},c:[\"실험에는 4가지 GPT 계열 모델(2.8B~13B)을 사용되었으며, 각각 Instruction-Tuning을 적용한 경우와 적용하지 않은 경우로 나누어 성능을 비교했습니다. 각 모델은 11,802개의 샘플로 구성된 sFIOG 데이터셋을 3회 반복 학습하였으며, 성능 평가를 위해 ROUGE-L, BERTScore와 같은 자동화된 평가 지표를 사용했습니다. \"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/mNwI3QuZzvoDCNCTzK18vJTJ0.png?width=4400&height=834\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"테스트 데이터셋은 세 가지로 구분되었습니다:\"]},{t:\"ol\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Type#1\"]},\": 학습 중에 등장한 기업과 질문을 포함하되, 새로운 답변으로 구성된 데이터셋\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Type#2\"]},\": 학습 중 등장한 기업이지만, 새로운 질문-답변 쌍으로 구성된 데이터셋\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Type#3\"]},\": 학습 중에 등장하지 않은 기업과 새로운 질문-답변 쌍으로 구성된 데이터셋\"]}]}]},{t:\"p\",a:{},c:[\"이러한 설정을 통해 모델이 얼마나 새로운 환경에 적응하며, 금융 추론을 일관되게 수행할 수 있는지를 평가했습니다.\"]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"1. Model Scale and Financial Reasoning\"]}]},{t:\"p\",a:{},c:[\"실험 결과, \",{t:\"strong\",a:{},c:[\"모델의 금융 추론 능력은 2.8B에서 6B 파라미터 규모\"]},\" 사이에서 명확히 드러나기 시작하며, 모델 크기가 커질수록 그 성능이 꾸준히 향상되었습니다. 특히, 13B 파라미터 규모의 \",{t:\"strong\",a:{},c:[\"LLama 모델\"]},\"이 가장 뛰어난 성능을 보였으며, 이는 ROUGE-L(0.217)과 BERTScore(0.821) 점수에서 가장 높은 평균치를 기록했습니다. 이러한 성능 향상은 두 가지 요인으로 설명될 수 있습니다:\"]},{t:\"ol\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"큰 모델일수록 더 많은 데이터를 학습\"]},\"하여 금융에 대한 지식이 풍부해집니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"모델의 아키텍처 자체가 더 큰 규모에서 더 나은 추론 능력\"]},\"을 발휘하게 됩니다. 특히, LLama 모델의 경우 학습된 정보들을 더 효과적으로 분석하고 종합하여 일관된 금융 추론을 수행하는 데 유리한 구조를 가지고 있음을 확인할 수 있었습니다.\"]}]}]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"sFIOG 데이터셋에서 얻어진 LLama, Galactica, GPT-J, Pythia 결과\"]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/wZUf0oNkshAT1qnybnspT40lLjY.png?width=4400&height=1045\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"2. Instruction-Tuning and Financial Reasoning\"]}]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Instruction-Tuning\"]},\"은 모든 모델의 성능을 향상시키는 데 중요한 역할을 했습니다. 하지만 그 개선 폭은 모델마다 달랐으며, 이는 Instruction-Tuning에 사용된 데이터셋의 차이에 기인할 수 있습니다. 특히 가장 작은 모델인 Pythia(2.8B)는 Instruction-Tuning을 적용하더라도 일관된 금융 추론 능력을 보여주지 못했습니다. 이는 \",{t:\"strong\",a:{},c:[\"금융 추론 능력이 일정한 모델 크기 이상에서 발현되는 특성\"]},\"임을 시사합니다. 즉, 작은 규모의 모델은 적절한 Instruction-Tuning이 이루어지더라도 복잡한 금융 추론을 처리하는 데 한계가 있음을 알 수 있습니다.\"]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"3. Dataset and Financial Reasoning\"]}]},{t:\"p\",a:{},c:[\"각 데이터셋 유형별로 모델 성능을 비교한 결과, \",{t:\"strong\",a:{},c:[\"새로운 질문-답변 쌍\"]},\"을 처리하는 \",{t:\"strong\",a:{},c:[\"type#2 데이터셋\"]},\"에서 모델들이 가장 약한 성능을 보였습니다. 학습 중 등장하지 않은 기업을 다루는 \",{t:\"strong\",a:{},c:[\"type#3 데이터셋\"]},\"이 가장 어려운 과제일 것이라 예상되었지만, 실제로는 \",{t:\"strong\",a:{},c:[\"익숙한 기업에 대한 새로운 질문을 다루는 것이 더 어려운 과제\"]},\"임이 발견되었습니다. 이는 **비정상적이거나 변화하는 지식(non-stationary knowledge)**이 모델의 일반화 능력에 영향을 미치며, 기존 지식을 새로운 상황에 적응시키는 데 어려움을 겪을 수 있음을 보여줍니다.\"]},{t:\"p\",a:{},c:[\"추가로, \",{t:\"strong\",a:{},c:[\"LLama 13B 모델\"]},\"을 다양한 데이터셋 크기와 학습 단계를 기준으로 평가한 결과, 데이터셋 크기와 학습 단계가 성능에 미치는 영향도 확인할 수 있었습니다. LLama는 더 많은 학습 단계를 거칠수록 성능이 개선되었지만, \",{t:\"strong\",a:{},c:[\"작은 데이터셋\"]},\"으로 학습한 경우에도 \",{t:\"strong\",a:{},c:[\"Instruction-Tuned GPT-J 모델\"]},\"보다 우수한 성능을 보였습니다. 이는 \",{t:\"strong\",a:{},c:[\"모델 크기\"]},\"가 금융 추론을 생성하는 데 중요한 요인임을 나타내며, \",{t:\"strong\",a:{},c:[\"데이터셋 크기는 상대적으로 덜 중요\"]},\"할 수 있음을 시사합니다.\"]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"4. Human Preference\"]}]},{t:\"p\",a:{},c:[\"최종적으로 인간 평가자들이 각 모델이 생성한 투자 의견을 비교했습니다. 평가자들은 \",{t:\"strong\",a:{},c:[\"LLama 모델\"]},\"이 가장 일관성 있고 관련성 높은 내용을 생성한다고 평가했으며, 특히 \",{t:\"strong\",a:{},c:[\"coherence(일관성), relevance(관련성), fluency(유창성)\"]},\" 면에서 다른 모델들보다 우세하다는 결과가 나왔습니다. 이러한 결과는 앞서 살펴본 자동화된 평가 지표인 ROUGE-L과 BERTScore 에서도 일관되게 나타났습니다.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"생성된 샘플에 대한 Human preference. 곤색은 LLama, 초록색은 Galatica, 파란색은 GPT-J, 노란색은 Pythia.\"]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/3QuaKLfnrL0qZSS2XWNQBP4S0k.png?width=4400&height=1228\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-8\"},c:[{t:\"strong\",a:{},c:[\"Conclusion\"]}]},{t:\"p\",a:{},c:[\"“Beyond Classification: Financial Reasoning in State-of-the-Art Language Models” 논문은 금융 분야에서 언어 모델이 수행할 수 있는 금융 추론 능력을 심층적으로 분석한 최초의 논문으로, 모델 사이즈, In-Context Question Answering, Instruction-tuning의 효과 등을 포함해 다방면으로 모델의 금융 추론 능력을 분석하였습니다.\"]},{t:\"p\",a:{},c:[\"실험 결과, 모델들은 어느 정도 금융 추론 능력을 가지고 있지만, 언어 모델이 인간 평가자와 동일한 수준의 평가를 수행하지 못하는 한계도 드러났습니다. 이는 언어 모델이 금융 도메인에서 완전히 신뢰할 수 있는 평가 도구로 사용되기에는 아직 부족함이 있음을 시사합니다.\"]},{t:\"p\",a:{},c:[\"결론적으로, 해당 연구는 \",{t:\"strong\",a:{},c:[\"금융 추론 분야에서 언어 모델의 가능성을 입증\"]},\"하며, 더 나아가 \",{t:\"strong\",a:{},c:[\"대형 언어 모델의 금융 도메인 적용 가능성\"]},\"을 넓히는 데 중요한 첫걸음이 되었습니다. 향후 더 큰 규모의 모델과 다양한 데이터셋을 활용한 연구를 통해, 언어 모델이 금융 전문가들의 의사결정 과정에서 중요한 도구로 자리 잡을 수 있기를 기대합니다.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"Reference\"]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"📝 Paper\"]},\": \",{t:\"a\",a:{href:\"https://arxiv.org/abs/2305.01505\"},c:[\"Beyond Classification: FInancial Reasoning in State-of-the-Art Language Models\"]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"📜 Dataset\"]},\": \",{t:\"a\",a:{href:\"https://huggingface.co/datasets/amphora/sFIOG\"},c:[\"amphora/sFIOG\"]}]}]}]}],toc:[{id:\"section-1\",text:\"LLM의 등장과 금융 추론에서의 활용\"},{id:\"section-2\",text:\"Introduction\"},{id:\"section-3\",text:\"FIOG(Financial Investment Opinion Generation)\"},{id:\"section-4\",text:\"Dataset Creation\"},{id:\"section-5\",text:\"\"},{id:\"section-6\",text:\"In-Context Question Answering\"},{id:\"section-7\",text:\"Experiments\"},{id:\"section-8\",text:\"Conclusion\"}],images:[\"https://framerusercontent.com/images/EnU0yytARDc1IA6pIOhPzRTlaFs.png?width=1920&height=180\",\"https://framerusercontent.com/images/4YOwRQ54gleLU1nfahIbKfNJMvs.png?width=4400&height=605\",\"https://framerusercontent.com/images/rPZtP0rRnpgPi5O5VK6hN9w8.png?width=4400&height=1168\",\"https://framerusercontent.com/images/mNwI3QuZzvoDCNCTzK18vJTJ0.png?width=4400&height=834\",\"https://framerusercontent.com/images/wZUf0oNkshAT1qnybnspT40lLjY.png?width=4400&height=1045\",\"https://framerusercontent.com/images/3QuaKLfnrL0qZSS2XWNQBP4S0k.png?width=4400&height=1228\"],readMins:13},{id:24,page:\"Journal 25\",slug:\"hae-rae-bench\",kind:\"research\",topic:\"Evaluation\",title:\"HAE-RAE Bench: 한국어 LLM의 문화적 이해도를 측정하다\",originalTitle:\"HAE-RAE Bench: 한국어 LLM의 문화적 이해도를 측정하다\",summary:\"한국어와 문화적 지식을 평가하는 HAE-RAE Bench\",language:\"ko\",date:\"Oct 11, 2024\",dateISO:\"2024-10-11\",source:\"https://onelineai.com/en/blog-research/hae-rae-bench\",path:\"/research/hae-rae-bench\",nodes:[{t:\"blockquote\",a:{},c:[{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:['\"',{t:\"a\",a:{href:\"https://arxiv.org/abs/2309.02706\"},c:[{t:\"strong\",a:{},c:[\"HAE-RAE Bench: Evaluation of Korean Knowledge in Language Models\"]}]},'\" 논문을 기반으로 한국어 언어모델의 한국 특화 지식 및 문화적 맥락을 평가하는 HAE-RAE Bench에 관한 내용을 다루고 있습니다. 해당 논문은 ',{t:\"a\",a:{href:\"https://lrec-coling-2024.org/\"},c:[{t:\"strong\",a:{},c:[\"LREC-COLING 2024\"]}]},\" 학회에 게재되었습니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"이 시리즈는 원라인에이아이 AI에서 발표한 논문을 리뷰합니다. 논문과 관련해서 궁금한 내용이 있다면 \",{t:\"a\",a:{href:\"https://www.onelineai.com/contact\"},c:[{t:\"strong\",a:{},c:[\"원라인에이아이 AI팀\"]}]},\"에게 문의주시기 바랍니다.\"]}]}]}]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-1\"},c:[{t:\"strong\",a:{},c:[\"다국어 LLM의 등장과 다국어 벤치마크의 문제점\"]}]},{t:\"p\",a:{},c:[\"최근 대형 언어 모델(LLM)이 다양한 언어와 작업에서 놀라운 성능을 보여주며 많은 관심을 받고 있습니다. 그러나 이러한 모델들은 주로 영어를 기반으로 학습되어, 한국어와 같은 다른 언어에서는 그 성능이 충분히 발휘되지 않는 경우가 많습니다. 이를 따라서 다양한 다국어 LLM이 개발되기 시작하며, 이러한 다국어 LLM을 평가하기 위한 다국어 벤치마크 또한 개발되기 시작했습니다. 하지만 기존의 다국어 벤치마크는 주로 영어 평가 데이터를 번역하거나 단순 재구성하는 방식으로 이루어져, 해당 언어의 문화적 맥락이나 고유한 언어적 특성을 충분히 반영하지 못한다는 한계가 있었습니다.\"]},{t:\"p\",a:{},c:[\"“\",{t:\"a\",a:{href:\"https://arxiv.org/abs/2309.02706\"},c:[{t:\"strong\",a:{},c:[\"HAE-RAE Bench: Evaluation of Korean Knowledge in Language Models\"]}]},\"” 논문에서는 이러한 기존 다국어 벤치마크의 평가 역량을 지적하면서, 한국어 특화 벤치마크인 \",{t:\"strong\",a:{},c:[\"HAE-RAE Bench\"]},\"를 제안했습니다. HAE-RAE Bench는 한국어에 특화된 지식과 문화적 맥락을 평가하기 위한 데이터셋으로, 어휘, 역사, 일반 상식, 독해 능력 등 6가지 작업을 포함하고 있습니다. 특히, 이 벤치마크는 기존의 한국어 평가 데이터셋들이 다루지 못했던 깊이 있는 한국 문화와 맥락을 평가할 수 있는 점에서 차별화됩니다.\"]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"HAE-RAE Bench 예시\"]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/p5YUrAUwaszL8NqFBpPOQQhwNg.png?width=4404&height=1237\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"p\",a:{},c:[\"HAE-RAE Bench를 활용한 실험에서 GPT-4와 같은 최신 다국어 언어 모델들과 비교하여, 한국어를 원어로 학습한 모델인 Polyglot-Ko가 얼마나 더 우수한 성능을 보이는지 분석하며, 다국어 모델들이 한국어와 같은 특정 언어의 문화적 지식을 제대로 학습하지 못하는 한계를 지적했습니다.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-2\"},c:[{t:\"strong\",a:{},c:[\"HAE-RAE Bench\"]}]},{t:\"p\",a:{},c:[\"다국어 LLM 개발 시에 가장 중요한 질문 중 하나는 “LLM이 특정 언어에 얼마나 깊이 있게 학습되었는가?”입니다. 많은 다국어 LLM이 다양한 언어에서 잘 작동할 수 있도록 설계되었지만, 이들이 진정으로 해당 언어의 문화적, 역사적 맥락까지 깊이 이해하는지는 아직 명확하지 않습니다. 특히 한국어와 같은 언어는 그 특유의 문법 구조와 어휘, 문화적 뉘앙스를 제대로 이해해야만 원활한 대화가 가능하기 때문에, 단순한 언어 처리 능력 이상의 평가가 필요합니다.\"]},{t:\"p\",a:{},c:[\"이러한 문제를 해결하고자, HAE-RAE Bench는 기존의 한국어 평가 도구들이 단순한 자연어 이해(NLU)나 논리적 추론 능력에만 집중한 것과 달리, 한국어 고유의 지식과 문화적 맥락을 얼마나 깊이 이해하고 있는지에 중점을 두고 평가하도록 제작되었습니다. 여기에는 한국어 어휘, 문화, 지리, 역사 등의 지식들이 포함됩니다.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"HAE-RAE Bench 구성\"]}]},{t:\"p\",a:{},c:[\"HAE-RAE Bench는 크게 6개의 주요 작업으로 구성됩니다:\"]},{t:\"ol\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"외래어\"]},\": 외래어와 그에 대응하는 한국어 단어를 구분하는 능력을 평가합니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"표준 명칭\"]},\": 특정 분야에서 공식적으로 채택된 용어를 정확히 선택할 수 있는지 평가합니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"희귀어\"]},\": 잘 알려지지 않은 한국어 단어의 의미를 올바르게 이해하고 사용하는 능력을 평가합니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"일반 상식\"]},\": 한국의 전통, 법, 지리, 대중문화(K-pop, 드라마 등)에 대한 지식을 평가합니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"역사\"]},\": 한국의 역사적 사건에 대한 이해도를 평가합니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"독해력\"]},\": 주어진 텍스트를 읽고 그 내용을 이해하는 능력을 평가합니다.\"]}]}]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"HAE-RAE Bench Statistics\"]}]},{t:\"p\",a:{},c:[\"HAE-RAE Bench의 Fertility Rate를 분석하여 각 모델이 한국어 단어를 어떻게 처리하는지 평가했습니다. 여기서 Fertility는 단어 당 생성되는 sub-token(하위 토큰)의 평균 수를 측정한 값으로, 이 수치가 높을수록 해당 모델이 한국어 어휘를 이해하는데 어려움을 겪을 가능성이 큽니다. Polyglot-Ko, Llama-2 등의 모델의 Fertility를 비교한 결과, 한국어를 중심으로 학습된 Polyglot-Ko 모델이 Llama-2보다 더 낮은 Fertility Rate를 보이며 한국어 어휘를 더 잘 처리함을 알 수 있습니다.\"]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"HAE-RAE Bench 통계\"]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/6yegdboEXDVUsPVaZLQU7PalAg.png?width=4404&height=852\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"HAE-RAE Bench Quality Check\"]}]},{t:\"p\",a:{},c:[\"논문의 저자들이 전체 HAE-RAE Bench 질문을 검수하여 레이블링 오류 또는 크롤링 오류를 수정했습니다. 이러한 검수 절차 덕분에 HAE-RAE Bench는 높은 신뢰성과 정확성을 보장합니다. HAE-RAE Bench의 고품질 및 목적성은 한국어 LLM의 한국어 특화 지식을 정확하게 평가하는 목적에 부합하게 사용될 수 있다는 것을 보여줍니다.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-3\"},c:[{t:\"strong\",a:{},c:[\"Experiments\"]}]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"Experimental Setup\"]}]},{t:\"p\",a:{},c:[\"4개의 모델 카테고리에서 총 10개의 모델을 선정하여 평가했습니다:\"]},{t:\"ol\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Korean-focused 모델\"]},\": Polyglot-Ko (1.3B, 3.8B, 5.8B, 12.8B) → 한국어 텍스트만으로 학습된 한국어 특화 LLM\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Multilingual 모델\"]},\": UMT5-XL, UMT5-XXL → 다중 언어 데이터로 학습된 LLM\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"English-centric 모델\"]},\": Llama-2 (7B, 13B) → 다중 언어 데이터로 학습된 LLM\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"State-of-the-art 모델\"]},\": GPT-3.5-Turbo, GPT-4 → 상용 모델\"]}]}]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"1. HAE-RAE Bench의 난이도\"]}]},{t:\"p\",a:{},c:[\"Polyglot-Ko 모델이 UMT5나 Llama-2에 비해 월등히 높은 성능을 보였습니다. 특히, Polyglot-Ko의 가장 작은 모델인 1.3B 모델이 훨씬 큰 규모의 UMT5와 Llama-2 모델보다 뛰어난 성능을 보였습니다. 이는 \",{t:\"strong\",a:{},c:[\"언어별 특화 코퍼스가 모델\"]},\"문화적 맥락 이해에 중요한 역할을 한다는 점을 보여줍니다.\"]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Loan Words, Standard Nomenclature, Rare Word task에 대한 평가 결과\"]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/I6ylVb0egE8FA9No4RUxMq96s64.png?width=4404&height=953\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"History, General Knowledge, Reading Comprehension tasks에 대한 평가 결과\"]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/ay7IQx6kY7cc5gHAUBwtGbU5os.png?width=4404&height=953\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"2. KoBEST와의 비교\"]}]},{t:\"p\",a:{},c:[\"HAE-RAE Bench는 KoBEST보다 더 높은 난이도를 가집니다. \",{t:\"strong\",a:{},c:[\"Polyglot-Ko\"]},\"와 \",{t:\"strong\",a:{},c:[\"UMT5\"]},\", \",{t:\"strong\",a:{},c:[\"Llama-2\"]},\" 모델의 성능 차이는 KoBEST에서보다 HAE-RAE Bench에서 훨씬 크게 나타났습니다. 특히, 5-shot과 10-shot 세팅에서도 외국어 LLM의 성능 향상이 미미해, 한국어 문화 지식에 기반한 태스크는 \",{t:\"strong\",a:{},c:[\"in-context learning\"]},\"과 한국어 학습 데이터로 쉽게 해결되지 않는다는 점이 확인되었습니다.\"]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"0-shot 세팅에서 HAE-RAE Bench와 KoBEST 간의 성능 차이\"]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/RzhqhDRU8vAJxedrISie8ZalymE.png?width=4404&height=953\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"5-shot 세팅에서 HAE-RAE Bench와 KoBEST 간의 성능 차이\"]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/mcP4u6gqUjn9ae47cvOjcZus.png?width=4404&height=953\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"10-shto 세팅에서 HAE-RAE Bench와 KoBEST간의 성능 차이\"]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/r9ACIKP9l0x0QPvTdwRwt0c.png?width=4404&height=953\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"3. 모델의 크기와 한국어 학습 데이터의 영향\"]}]},{t:\"p\",a:{},c:[\"UMT5는 대량의 한국어 토큰을 사용해서 학습되었음에도 불구하고, HAE-RAE Bench에서 Llama-2보다 낮은 성능을 보여줬습니다. 또한 앞서 살펴봤듯이 in-context learning의 효과도 미미했습니다. 이러한 결과는 LLM의 language-specific 추론 능력이 pretraining에 사용된 해당 언어의 토큰 수와 연관되어 있지 않다는 것을 의미합니다.\"]},{t:\"p\",a:{},c:[\"또한 HAE-RAE Bench는 모델의 크기에 따라 선형적으로 성능이 증가하던 KoBEST와 달리 모델 크기만으로 성능을 설명할 수 없었습니다. Polyglot-Ko 모델에 대해 선형회귀를 적용했을 때, KoBEST에서는 굉장히 높은 R2 값을 보여줬지만, HAE-RAE Bench의 경우에는 굉장히 낮은 R2 값을 보여줬습니다. HAE-RAE Bench에 대한 선형회귀 및 ANOVA 분석의 낮은 통계적 유의성은 해당 벤치마크의 평가 결과가 모델 크기 뿐만 아니라 더 다양한 요인으로부터 영향을 받는다는 것을 암시합니다.\"]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"HAE-RAE Bench와 KoBEST에 대한 선형 회귀 및 ANOVA 분석 결과\"]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/ChBI1ju928UlMJ3VDXyL9oA.png?width=4404&height=779\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"4. GPT-3.5와 GPT-4의 HAE-RAE Bench 성능\"]}]},{t:\"h5\",a:{},c:[\"GPT-3.5와 GPT-4의 경우에도 HAE-RAE Bench에서의 성능이 KoBEST에 비해 낮았습니다. GPT-4는 **67.8%**의 정확도를 기록했지만, 이는 여전히 Polyglot-Ko-12.8B 모델에 비해 낮은 성과였습니다. 이러한 결과는 영어 기반의 학습에서 얻은 지식이 한국어처럼 \",{t:\"strong\",a:{},c:[\"맥락\"]},\"이 중요한 언어에서 제대로 전이되지 않는다는 것을 시사합니다.\"]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"HAE-RAE Bench와 KoBEST에서 GPT-3.5-Turbo와 GPT-4의 zero-shot 성능\"]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/qlgLTL4LajUC1JSVATsviei4gE.png?width=4404&height=742\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"5. 영어로부터의 지식 전이\"]}]},{t:\"p\",a:{},c:[\"GPT-3.5-Turbo와 GPT-4가 영어 코퍼스로부터 얻어진 능력을 HAE-RAE Bench를 푸는데 사용할 수 있는지 실험을 진행했습니다. 이를 위해서 영어로 학습된 능력을 다른 언어로 전이시키는 prompting 방법인 XLT를 사용하여 실험을 진행한 결과, HAE-RAE Bench와 KoBEST 모두에서 성능 향상이 일어났습니다. 하지만 KoBEST에서 이 성능 향상이 더 크게 발생했는데, 이는 KoBEST가 중점적으로 다루는 언어 이해 및 추론 능력이 더욱 잘 전이된다는 것을 암시합니다. 반대로 HAE-RAE Bench는 문화적 지식 및 뉘앙스를 평가하는데, 이러한 능력들은 영어 토큰으로부터 학습되기 어렵다는 것을 알 수 있습니다.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-4\"},c:[{t:\"strong\",a:{},c:[\"Conclusion\"]}]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"HAE-RAE Bench\"]},\"는 한국어를 비롯한 다국어 모델 평가의 새로운 기준을 제시합니다. 기존의 다국어 평가 벤치마크는 영어 벤치마크의 번역 또는 단수 변형을 활용한 자연어 이해나 논리적 추론에 집중했지만, HAE-RAE Bench는 한국어 모델이 얼마나 깊이 있는 한국 문화적 지식을 보유하고 있는지를 평가하는 데 중점을 두고 있습니다. 이를 통해 단순히 크기나 다국어 학습 능력에 의존하지 않고, 모델의 한국어 이해력과 문화적 맥락에 대한 적응성을 평가할 수 있습니다. 특히, 한국어가 아닌 언어로 학습된 다국어 모델들에게 이 데이터셋은 상당한 도전 과제를 제시하며, 향후 연구와 개발에 중요한 방향성을 제시할 것입니다.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"Reference\"]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"📝 \",{t:\"strong\",a:{},c:[\"Paper\"]},\": \",{t:\"a\",a:{href:\"https://arxiv.org/abs/2309.02706\"},c:[\"HAE-RAE Bench: Evaluation of Korean Knowledge in Language Models\"]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"📜 \",{t:\"strong\",a:{},c:[\"HAE-RAE-Bench-1.0:\"]},\" \",{t:\"a\",a:{href:\"https://huggingface.co/datasets/HAERAE-HUB/HAE_RAE_BENCH_1.0\"},c:[\"HAERAE-HUB/HAE_RAE_BENCH_1.0\"]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"📜 \",{t:\"strong\",a:{},c:[\"HAR-RAE-Bench-1.1\"]},\": \",{t:\"a\",a:{href:\"https://huggingface.co/datasets/HAERAE-HUB/HAE_RAE_BENCH_1.1\"},c:[\"HAERAE-HUB/HAE_RAE_BENCH_1.1\"]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"📜 \",{t:\"strong\",a:{},c:[\"HAE-RAE-Bench-2.0\"]},\": \",{t:\"a\",a:{href:\"https://huggingface.co/datasets/HAERAE-HUB/HAE_RAE_BENCH_2.0\"},c:[\"HAERAE-HUB/HAE_RAE_BENCH_2.0\"]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"🖥️ \",{t:\"strong\",a:{},c:[\"GitHub\"]},\": \",{t:\"a\",a:{href:\"https://github.com/HAE-RAE/HAE-RAE-BENCH\"},c:[\"HAE-RAE/HAE-RAE-BENCH\"]}]}]}]}],toc:[{id:\"section-1\",text:\"다국어 LLM의 등장과 다국어 벤치마크의 문제점\"},{id:\"section-2\",text:\"HAE-RAE Bench\"},{id:\"section-3\",text:\"Experiments\"},{id:\"section-4\",text:\"Conclusion\"}],images:[\"https://framerusercontent.com/images/p5YUrAUwaszL8NqFBpPOQQhwNg.png?width=4404&height=1237\",\"https://framerusercontent.com/images/6yegdboEXDVUsPVaZLQU7PalAg.png?width=4404&height=852\",\"https://framerusercontent.com/images/I6ylVb0egE8FA9No4RUxMq96s64.png?width=4404&height=953\",\"https://framerusercontent.com/images/ay7IQx6kY7cc5gHAUBwtGbU5os.png?width=4404&height=953\",\"https://framerusercontent.com/images/RzhqhDRU8vAJxedrISie8ZalymE.png?width=4404&height=953\",\"https://framerusercontent.com/images/mcP4u6gqUjn9ae47cvOjcZus.png?width=4404&height=953\",\"https://framerusercontent.com/images/r9ACIKP9l0x0QPvTdwRwt0c.png?width=4404&height=953\",\"https://framerusercontent.com/images/ChBI1ju928UlMJ3VDXyL9oA.png?width=4404&height=779\",\"https://framerusercontent.com/images/qlgLTL4LajUC1JSVATsviei4gE.png?width=4404&height=742\"],readMins:9},{id:25,page:\"Journal 26\",slug:\"krx-bench\",kind:\"research\",topic:\"Finance\",title:\"GPT-4를 활용한 금융 도메인 벤치마크 자동화: KRX-Bench의 등장\",originalTitle:\"GPT-4를 활용한 금융 도메인 벤치마크 자동화: KRX-Bench의 등장\",summary:\"금융 도메인 평가를 위한 KRX-Bench 설계\",language:\"ko\",date:\"Oct 15, 2024\",dateISO:\"2024-10-15\",source:\"https://onelineai.com/en/blog-research/krx-bench\",path:\"/research/krx-bench\",nodes:[{t:\"blockquote\",a:{},c:[{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:['\"',{t:\"a\",a:{href:\"https://aclanthology.org/2024.finnlp-1.2.pdf\"},c:[{t:\"strong\",a:{},c:[\"KRX-Bench: Automating Financial Benchmark Creation via Large Language Models\"]}]},'\" 논문을 기반으로 한국어 언어모델의 한국 특화 지식 및 문화적 맥락을 평가하는 HAE-RAE Bench에 관한 내용을 다루고 있습니다. 해당 논문은 ',{t:\"a\",a:{href:\"https://sites.google.com/nlg.csie.ntu.edu.tw/finnlp-kdf-2024/home\"},c:[\"LREC-COLING 2024 FinNLP\"]},\" 학회에 게재되었습니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"이 시리즈는 원라인에이아이 AI에서 발표한 논문을 리뷰합니다. 논문과 관련해서 궁금한 내용이 있다면 \",{t:\"a\",a:{href:\"https://www.onelineai.com/contact\"},c:[{t:\"strong\",a:{},c:[\"원라인에이아이 AI팀\"]}]},\"에게 문의주시기 바랍니다.\"]}]}]}]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-1\"},c:[{t:\"strong\",a:{},c:[\"금융 도메인 언어 모델의 등장과 평가의 필요성\"]}]},{t:\"p\",a:{},c:[\"오늘날 대형 언어 모델(LLM)의 발전은 다양한 산업의 업무를 혁신하고 있습니다. 그러나 금융 분야에서는 LLM이 생성하는 정보의 정확성과 신뢰도가 무엇보다 중요합니다. 잘못된 정보는 오판을 유발하여 막대한 손실을 초래할 수 있기 때문입니다.\"]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"한국거래소\"]},\"(\",{t:\"strong\",a:{},c:[\"KRX\"]},\")와 \",{t:\"strong\",a:{},c:[\"OneLineAI\"]},\"가 공동 집필한 “\",{t:\"a\",a:{href:\"https://aclanthology.org/2024.finnlp-1.2.pdf\"},c:[{t:\"strong\",a:{},c:[\"KRX-Bench: Automating Financial Benchmark Creation via Large Language Models\"]}]},\"” 논문에서는 이러한 금융 분야 LLM의 문제를 지적하면서, 벤치마크 자동 생성 파이프라인 \",{t:\"strong\",a:{},c:[\"KRX-Bench\"]},\"를 제안하였습니다.\"]},{t:\"p\",a:{},c:[\"KRX-Bench는 GPT-4를 활용하여 실제 기업의 연간 보고서, 금융 소송 문서 등 다양한 금융 자료로부터 자동으로 질문을 생성합니다. 해당 파이프라인을 통해 생성된 벤치마크 데이터셋은 다양한 금융 주제를 다루며, 단순한 사실 확인부터 복잡한 금융 추론까지 다양한 난이도의 문제를 포함하고 있습니다. 또한, 다국어 지원과 개방형 질문 등 다양한 형태의 문제를 제공하여 언어 모델의 종합적인 능력을 평가할 수 있습니다.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-2\"},c:[{t:\"strong\",a:{},c:[\"KRX-Bench\"]}]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"KRX-Bench: 벤치마크 생성 파이프라인\"]}]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"KRX-Bench\"]},\"는 금융 분야의 LLM 평가를 위한 자동 벤치마크 생성 파이프라인입니다. KRX-Bench는 GPT-4-Turbo를 활용하여 기존 문서로부터 다음과 같이 크게 3가지의 프로세스로 벤치마크 데이터를 생성합니다:\"]},{t:\"ol\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"질문 및 답변 생성\"]},\":\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"GPT-4-Turbo에 문서를 제공하고 Q&A 쌍을 생성하도록 지시\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"이 단계에서는 연간 보고서, 금융 소송 문서 등 \",{t:\"em\",a:{},c:[{t:\"strong\",a:{},c:[\"다양한 금융 자료를 입력으로 사용\"]}]}]}]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"오답 선택지 생성\"]},\":\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"생성된 Q&A 쌍을 다지선다형 문제로 변환하기 위해 \",{t:\"em\",a:{},c:[{t:\"strong\",a:{},c:[\"오답 선택지\"]}]},\" 제작\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"BM25 알고리즘을 사용해 유사한 질문을 찾고, GPT-4-Turbo가 이를 바탕으로 그럴듯한 오답 생성\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"질문과 무관한 회사를 언급하거나 길이가 크게 다른 선택지는 제거하는 등의 휴리스틱 규칙을 적용하여 보기의 품질을 관리\"]}]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"품질 관리\"]},\":\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"GPT-4-Turbo를 활용하여 \",{t:\"em\",a:{},c:[{t:\"strong\",a:{},c:[\"답변 불가능한 질문을 식별하고 제거\"]}]},\" → False Positive 비율을 1%로 낮춤\"]}]}]}]}]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"KRX-Bench-POC: 벤치마크 데이터셋\"]}]},{t:\"p\",a:{},c:[\"파이프라인의 효과를 검증하기 위해 KRX-Bench-POC라는 벤치마크 데이터셋을 생성하여 테스트를 진행하였습니다. 해당 벤치마크 데이터셋은 다음과 같은 특징을 가집니다:\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"미국, 일본, 한국의 연간 보고서를 기반으로 생성\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"총 1,003개의 질문 (미국 373개, 한국 319개, 일본 311개)\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"15개의 다양한 카테고리(주요 사업, 사업 목표, 회사 미션 등)로 구성\"]}]}]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"KRX-Bench-POC에 속해 있는 벤치마크 데이터셋 예시\"]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/ESyu7u8Bda8D18kc0qiaAJ8KEX0.png?width=1584&height=320\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"p\",a:{},c:[\"벤치마크 데이터셋의 품질 평가는 다음과 같이 진행했습니다:\"]},{t:\"ol\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"머신 러닝 특징 테스트\"]},\":\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Similarity-based\"]},\": BM-25 & BERT를 사용하여 질문과 보기 간에 유사도가 가장 높은 보기를 답으로 고려\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Zero-shot Classifier\"]},\": NLI 태스크로 학습된 분류기 사용\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"KRX-Bench-POC는 기존의 HellaSwag 벤치마크와 비교하여 유사하거나 더 낮은 점수를 보여, 과도한 패턴이나 단서가 없음을 확인했습니다.\"]}]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"사람 평가\"]},\":\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"GPT-4-Turbo의 질문 분류와 인간 평가자의 판단을 비교했습니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"결과적으로 false positive(FP) 비율이 1%에 불과해, 생성된 질문의 높은 품질을 입증했습니다.\"]}]}]}]}]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-3\"},c:[{t:\"strong\",a:{},c:[\"Result\"]}]},{t:\"p\",a:{},c:[\"KRX-Bench-POC를 활용한 다양한 LLM의 평가 결과는 다음과 같은 인사이트를 제공합니다:\"]},{t:\"ol\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"모델 크기와 성능의 관계\"]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"당시 최신 오픈소스 모델인 Qwen1.5-72B와 Llama-2-70B도 80% 미만의 점수를 기록하며 여전히 개선의 여지가 있음을 시사\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"KRX-Bench 파이프라인이 인간의 개입 없이도 최신 모델들을 도전적으로 평가할 수 있는 벤치마크를 성공적으로 생성했음을 입증\"]}]}]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"전체 데이터셋에 걸쳐서 3-shot setting으로 계산된 정확도의 평균\"]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/njlRNx1xvHy78QBbSwJ55yWxE.png?width=4400&height=1980\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"지역별 편향\"]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"모든 질문이 영어로 제시되었음에도 불구하고, 모델들은 미국 기업에 관한 질문에서 일본이나 한국 기업 관련 질문보다 더 높은 성능을 보임\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"이러한 결과는 영어로 된 일본 및 한국 기업 관련 자료의 부족, 추가 학습 과정에서의 catastrophic forgetting 등 여러 요인에 기인할 수 있습니다. 또한 더 어려운 문서(예: 내부 문서)를 입력으로 사용하면 벤치마크의 난이도를 쉽게 높일 수 있음을 시사\"]}]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"GPT-4-Turbo 자체 평가\"]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"KRX-Bench 파이프라인이 GPT-4-Turbo를 사용하여 생성되었다는 점에서, GPT-4-Turbo 자체의 벤치마크 수행 능력을 평가하는 것은 중요한 의미를 가짐\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"GPT-4-Turbo는 테스트된 모델 중 가장 높은 점수를 기록했지만, 모든 하위 집합에서 완벽한 성과를 보이지는 않음\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"특히 한국과 일본 기업 관련 질문에서 상대적으로 낮은 점수를 기록\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"이는 보충 자료를 활용한 데이터셋 생성 방식이 모델의 지식 범위를 넘어서는 질문을 만들어낼 수 있음을 시사\"]}]}]}]}]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-4\"},c:[{t:\"strong\",a:{},c:[\"KRX-Bench 구현 가이드\"]}]},{t:\"p\",a:{},c:[\"KRX-Bench 파이프라인은 단순히 다지선다형 문제를 생성하는 데 그치지 않고, 다양한 종류의 금융 벤치마크를 만드는 데 활용될 수 있습니다:\"]},{t:\"ol\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Open-ended Generation\"]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"KRX-Bench는 다지선다 문제에서 벗어나 언어 모델의 생성 능력을 평가하기 위한 개방형 질문을 만들 수 있습니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:['해당 벤치마크 데이터셋에 대한 평가는 \"LLM-as-a-Judge\" 접근법을 사용하여 답변의 품질을 1-5점 척도로 평가할 수 있습니다.']}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"이 방법은 모델의 생성 능력뿐만 아니라 지식의 깊이도 평가할 수 있어, 금융 분야 LLM의 종합적인 성능 측정에 유용합니다.\"]}]}]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"open-ended question 예시\"]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/CPGwb0anQVtmupxpqXcjvIvWc.png?width=4400&height=1677\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Multilinguality\"]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"KRX-Bench를 통해 한국어로 250개의 질문을 생성하고, 이를 영어에서 번역한 질문과 비교 평가했습니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"두 명의 평가자가 질문의 자연스러움과 품질을 비교한 결과, 직접 생성된 결과가 더 자연스럽고 품질 측면에서 좀 더 괜찮다는 것을 확인하였습니다.\"]}]}]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"generation과 translation 간의 win rate 비교 결과\"]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/zGXXCj4PumsRGLkBC0HGuPkWA.png?width=4400&height=1090\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Beyond Knowledge Benchmarks\"]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"KRX-Bench를 통해 한국의 금융 소송 관련 문서를 입력으로 사용하여 100개의 법적 추론 문제를 생성했습니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"평가 결과, GPT-4-Turbo가 거의 완벽한 점수를 받으며 입력 문서의 특성이나 오답 생성의 한계 때문일 수 있기 때문에 추가적인 연구가 필요합니다.\"]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Multilinguality & Reasoning Capability 평가 결과\"]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/lPhYO43yHOjLkEUQ3kR11gzhM0.png?width=4400&height=1086\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]}]}]}]}]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-5\"},c:[{t:\"strong\",a:{},c:[\"Conclusion\"]}]},{t:\"p\",a:{},c:[\"KRX-Bench는 금융 분야에서 LLM을 평가하기 위한 자동화 벤치마크 생성 파이프라인입니다. 이 연구의 주요 성과와 의의는 다음과 같습니다:\"]},{t:\"ol\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"자동화된 벤치마크 생성\"]},\": KRX-Bench는 GPT-4를 활용하여 실제 기업 보고서와 금융 문서로부터 고품질의 평가 문항을 자동으로 생성합니다. 이는 빠르게 변화하는 금융 시장에 대응하여 지속적으로 최신 벤치마크를 제공할 수 있는 가능성을 보여줍니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"다양성과 포괄성\"]},\": KRX-Bench-POC를 통해 미국, 일본, 한국의 기업들을 아우르는 다국적 벤치마크를 구축했습니다. 이는 LLM의 글로벌 금융 지식을 평가하는 데 중요한 도구가 될 수 있습니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"품질 검증\"]},\": 엄격한 품질 관리 프로세스를 통해 생성된 벤치마크의 신뢰성을 확보했습니다. 특히 1%의 낮은 오류율은 이 방법론의 효과성을 입증합니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"다양한 응용 가능성\"]},\": 개방형 질문 생성, 다국어 지원, 법적 추론 문제 생성 등 KRX-Bench의 다양한 응용 사례를 통해 이 파이프라인의 유연성과 확장성을 확인했습니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"LLM 성능 평가\"]},\": 최신 LLM들의 성능을 평가한 결과, 여전히 개선의 여지가 있음을 확인했습니다. 특히 지역별 편향성 문제는 앞으로 해결해야 할 중요한 과제로 남아있습니다.\"]}]}]},{t:\"p\",a:{},c:[\"KRX-Bench는 금융 분야 LLM의 발전을 위한 중요한 도구가 될 것입니다. 이 연구는 LLM의 금융 지식과 추론 능력을 정확히 평가하고, 나아가 모델의 개선 방향을 제시하는 데 기여할 것입니다. 또한, KRX-Bench는 금융 분야에서 LLM의 신뢰성과 정확성을 높이는 데 중요한 역할을 할 것이며, 이는 궁극적으로 금융 산업에서 AI 기술의 안전하고 효과적인 활용을 촉진할 것입니다.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"Reference\"]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"📝 Paper\"]},\": \",{t:\"a\",a:{href:\"https://aclanthology.org/2024.finnlp-1.2.pdf\"},c:[\"KRX-Bench: Automating Financial Creation via Large Language Models\"]}]}]}]}],toc:[{id:\"section-1\",text:\"금융 도메인 언어 모델의 등장과 평가의 필요성\"},{id:\"section-2\",text:\"KRX-Bench\"},{id:\"section-3\",text:\"Result\"},{id:\"section-4\",text:\"KRX-Bench 구현 가이드\"},{id:\"section-5\",text:\"Conclusion\"}],images:[\"https://framerusercontent.com/images/ESyu7u8Bda8D18kc0qiaAJ8KEX0.png?width=1584&height=320\",\"https://framerusercontent.com/images/njlRNx1xvHy78QBbSwJ55yWxE.png?width=4400&height=1980\",\"https://framerusercontent.com/images/CPGwb0anQVtmupxpqXcjvIvWc.png?width=4400&height=1677\",\"https://framerusercontent.com/images/zGXXCj4PumsRGLkBC0HGuPkWA.png?width=4400&height=1090\",\"https://framerusercontent.com/images/lPhYO43yHOjLkEUQ3kR11gzhM0.png?width=4400&height=1086\"],readMins:7},{id:26,page:\"Journal 27\",slug:\"kmmlu\",kind:\"research\",topic:\"Evaluation\",title:\"KMMLU: 번역의 한계를 넘은 진짜 한국어 평가\",originalTitle:\"KMMLU: 번역의 한계를 넘은 진짜 한국어 평가\",summary:\"한국어의 실제 맥락을 평가하는 KMMLU\",language:\"ko\",date:\"Jan 8, 2025\",dateISO:\"2025-01-08\",source:\"https://onelineai.com/en/blog-research/kmmlu\",path:\"/research/kmmlu\",nodes:[{t:\"blockquote\",a:{},c:[{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:['\"',{t:\"a\",a:{href:\"https://arxiv.org/abs/2402.11548\"},c:[{t:\"strong\",a:{},c:[\"KMMLU: Measuring Massive Multitask Language Understanding in Korean\"]}]},'\" 논문을 기반으로 한국어 언어 모델의 한국 특화 지식 및 문화적 맥락 능력을 평가하는 \\bKMMLU에 관한 내용을 다루고 있습니다. 해당 논문은 ',{t:\"a\",a:{href:\"https://2025.naacl.org/\"},c:[\"\\bNAACL 2025\"]},\" 학회에 게재되었습니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"이 시리즈는 원라인에이아이 AI에서 발표한 논문을 리뷰합니다. 논문과 관련해서 궁금한 내용이 있다면 \",{t:\"a\",a:{href:\"https://www.onelineai.com/contact\"},c:[{t:\"strong\",a:{},c:[\"원라인에이아이 AI팀\"]}]},\"에게 문의주시기 바랍니다.\"]}]}]}]},{t:\"h2\",a:{id:\"section-1\"},c:[{t:\"strong\",a:{},c:[\"다국어 벤치마크의 한계와 KMMLU의 등장\"]}]},{t:\"p\",a:{},c:[\"최근 대규모 언어 모델(LLM)의 성장은 영어 뿐만 아니라 다양한 언어에서 활발하게 이루어지고 있습니다. 이에 반해서 성능 평가를 위한 벤치마크 개발은 영어에서 활발하게 이루어질 뿐, 다국어의 경우 대부분 영어 기반으로 설계된 평가 벤치마크 데이터를 타 언어로 번역하는 방식에 의존해오고 있습니다. 이러한 번역된 벤치마크는 본질적인 한계를 지니고 있습니다. 우선, 번역 과정에서 자연스러운 언어적 표현이 손실되거나 번역 오류, 문법적 실수 등이 발생할 수 있습니다. 또한, 영어 중심으로 설계된 질문들은 미국의 법률 체계, 영어권 문화와 관습 등을 기반으로 하고 있어, 번역된 데이터만으로는 각 언어의 고유한 문화적, 지역적 특성을 충분히 반영하지 못합니다.\"]},{t:\"p\",a:{},c:[\"한국어 평가 환경도 예외는 아니었습니다. 기존 한국어 벤치마크는 영어 벤치마크를 기계 번역하거나, 소수의 제한된 데이터로 구성된 경우가 많아, 한국어를 주요 언어로 사용하는 LLM의 능력을 평가하기에는 적합하지 않았습니다. 또한 다음의 표를 보면 알 수 있듯이, 언어 및 문화적 이해를 중점적으로 평가하는 벤치마크들에 대해 집중되어, 전문 지식 및 모델의 추론 능력을 평가하기 위한 벤치마크의 수가 부족합니다. 이러한 문제는 한국어 모델의 성능이 과소평가되거나, 실제 한국어 사용 환경을 반영하지 못하는 결과로 이어졌습니다.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/btDwW2OSnF8GM4yO5kbb9cwzlXw.png?width=1920&height=434\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"이런 맥락에서 저희 연구팀은 한국어와 한국 문화에 특화된 벤치마크의 필요성을 강조하며 해당 특성을 중점적으로 고려한 \",{t:\"strong\",a:{},c:[\"KMMLU\"]},\"(Korean Massive Multitask Language Understanding)를 개발하였습니다. KMMLU는 기존 번역된 벤치마크와 달리, 45개 분야에 걸쳐 35,030개의 전문가 수준의 다지선다형 질문으로 구성된 KMMLU는 기존 번역 중심의 평가 데이터와 달리, 한국어 시험 문제를 원문 그대로 수집해 언어적 자연스러움과 문화적 맥락을 온전히 반영하고 있습니다. 이 벤치마크는 기존의 다른 한국어 벤치마크와 비교해서 한국어를 기반으로 한 LLM의 성능을 보다 정확히 측정할 수 있는 새로운 기준을 제시합니다.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-2\"},c:[{t:\"strong\",a:{},c:[\"KMMLU: 한국어 벤치마크의 새로운 지평을 열다\"]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/vLCWtzNDxWVamhf62TcGtqOxAw.png?width=1920&height=952\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"KMMLU\"]},\"는 한국어로 작성된 전문가 수준의 MCQA (Multiple-Choice Question Answering) 문제 35,030개로 구성되어 있는 벤치마크입니다. KMMLU는 모든 질문을 한국어 시험에서 직접 수집하여 한국어와 한국 문화의 언어적, 문화적 특성을 온전히 반영한 벤치마크로, 위의 그림과 같이 인문학, 사회과학, STEM(과학, 기술, 공학, 수학), 응용과학 등 45개 분야를 아우르며, 그 중 일부는 한국의 국가 자격시험이나 공무원 시험과 같이 최소 수년 이상의 실무 경험을 요구하는 고난도 시험에서 추출되었습니다. 또한, 질문은 한국의 문화적\\xb7지역적\\xb7법적 지식을 필요로 하는 사례가 포함되어 있어, 단순 번역 기반의 벤치마크가 제공하지 못하는 한국어 모델 평가의 정밀성을 확보했습니다.\"]},{t:\"p\",a:{},c:[\"KMMLU의 주요 특징은 크게 두 가지로 나눌 수 있습니다.\"]},{t:\"ol\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"질문의 \",{t:\"strong\",a:{},c:[\"자연스러운 언어적 흐름\"]},\"과 \",{t:\"strong\",a:{},c:[\"한국 고유의 문화적 맥락\"]},\"을 보장했습니다. 이는 영어 기반 벤치마크를 번역한 데이터에서 종종 발생하는 비자연스러운 표현이나 문법 오류를 방지합니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"데이터셋과 평가 코드를 공개하여 \",{t:\"strong\",a:{},c:[\"투명성\"]},\"과 \",{t:\"strong\",a:{},c:[\"재현성\"]},\"을 높였습니다. 이를 통해 연구자들은 KMMLU를 활용해 한국어 대형 언어 모델(LLM)의 성능을 더욱 정확히 평가하고, 개선 방향을 구체화할 수 있습니다.\"]}]}]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"[Dataset Creation: KMMLU 데이터 구축]\"]}]},{t:\"p\",a:{},c:[\"KMMLU 데이터셋은 한국의 공무원시험(PSAT), 국가자격시험, 그리고 대학수학능력시험(CSAT)을 포함한 533개의 다양한 출처에서 수집된 371,002개의 질문으로 시작되었습니다. 이후, 데이터 품질을 최우선으로 고려하여 다음과 같은 정제 과정을 거쳤습니다:\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"자동 크롤링과 필터링\"]},\": 초기 데이터는 자동 크롤링을 통해 수집되었으며, 중복 제거와 구문 오류 제거를 위해 정규식 패턴, 스톱워드, 모델 기반 분류기를 활용한 휴리스틱 필터링이 적용되었습니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"형식 표준화\"]},\": 4개 미만의 선택지를 가진 질문은 제외하고, 4개 이상의 선택지를 가진 질문은 모두 표준화했습니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"데이터셋 축소\"]},\": 이 과정에서 데이터셋은 약 34%가 줄어들어, 최종적으로 243,777개의 질문이 확보되었습니다. 질문의 중복성을 제거하기 위해 시즌별로 반복된 질문 또한 대폭 축소되었습니다.\"]}]}]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"[Dataset Processing: KMMLU 데이터 전처리 및 분석]\"]}]},{t:\"p\",a:{},c:[\"KMMLU의 테스트셋은 사람 평가에서 가장 낮은 정확도를 기록한 질문들로 구성되어 있으며, 35,030개의 질문에 대해 전부 수작업으로 검토하여 저작권 문제가 있는 질문들을 교체하거나 제거하였습니다. 또한, 데이터셋의 약 90%는 실제 시험 응시자들의 성과 데이터를 포함하고 있으며, 평균 정확도는 약 62.6%로 나타났습니다. 이로 인해 KMMLU에서 80% 이상의 점수를 기록하는 것은 최소한 인간 전문가의 성과와 동등하다고 평가됩니다.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/AdK3OR8CQ8Cu2tZNRp7RkgoM.png?width=1920&height=612\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"추가적으로 KMMLU와 기존 영어 중심의 MMLU를 번역한 벤치마크 간의 한국 문화와 언어적 특성을 반영한 설계 차이를 분석하였습니다. 이를 위해 연구팀은 번역된 MMLU와 KMMLU를 다음 두 가지 측면에서 비교하였고, 비교 결과는 위 그래프와 같습니다:\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"문장의 자연스러움\"]},\": 한국어 화자들에게 자연스럽게 느껴지는 구성을 가지고 있는가? → KMMLU의 문항은 한국어 화자들에게 자연스럽게 느껴지는 구성을 가지고 있으며, 이는 번역된 MMLU와의 큰 차이점입니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"한국 지식 요구 수준\"]},\": 한국의 문화적, 사회적, 법적 지식을 요구하며, 한국 특유의 맥락을 평가할 수 있는가? → KMMLU의 약 20.4%의 문항은 한국의 문화적, 사회적, 법적 지식을 요구합니다. 이는 한국 특유의 맥락을 평가할 수 있는 유용한 지표를 제공합니다.\"]}]}]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"[KMMLU-HARD: 고난도 문제 평가]\"]}]},{t:\"p\",a:{},c:[\"KMMLU의 고난도 문제를 평가하기 위해 별도의 하위 세트인 \",{t:\"strong\",a:{},c:[\"KMMLU-Hard\"]},\"도 함께 설계되었습니다. 이는 GPT-4, HyperCLOVA X, Gemini Pro의 상업용 모델을 포하한 다양한 언어 모델이 정답을 맞추지 못한 4,014개의 문제만으로 구성된 데이터셋입니다. KMMLU-Hard는 LLM(Large Language Model)의 한계와 약점을 보다 구체적으로 파악할 수 있도록 설계되었습니다.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-3\"},c:[{t:\"strong\",a:{},c:[\"KMMLU 실험 결과 분석\"]}]},{t:\"h4\",a:{},c:[\"1. Pretraining Compute\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/95EtWE6lWRhpN30TjsQ9C8BNc0.png?width=1920&height=668\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"KMMLU 평가 결과는 모델의 성능이 모델의 사전 학습에 사용된 계산 자원(ExaFLOP, \",{t:\"code\",a:{},c:[\"10^18\"]},\" 부동소수점 연산으로 \",{t:\"code\",a:{},c:[\"6 \\xd7 #param \\xd7 #tokens\"]},\"으로 계산)에 비례하여 증가하는 경향을 보여줍니다. 위 그래프를 보면 알 수 있듯이 Polyglot-Ko는 한국어 특화 모델로 학습되었음에도 불구하고, 다른 모델들에 비해 더 적은 학습 비용으로 인해 성능이 크게 떨어지는 것을 확인할 수 있습니다. 이는 충분한 데이터와 계산 자원을 활용한 장기 학습이 모델의 언어 이해 능력을 강화하는 데 중요하다는 점을 시사합니다.\"]},{t:\"h3\",a:{id:\"section-4\"},c:[\"2. Fine-Tuning\"]},{t:\"p\",a:{},c:[\"사전 학습된 모델의 성능은 Fine-Tuning 후에도 크게 개선되지 않는 경향을 보였습니다. Llama-2-70B 및 Qwen-72B 모델에서는 Fine-Tuning 후 성능이 오히려 감소하는 (-3.55% 및 -5.81%) 것을 확인할 수 있었습니다. 이는 Fine-Tuning 과정에서 사용된 데이터셋이 다국어 또는 한국어 텍스트를 충분히 포함하지 못하거나, Fine-Tuning이 본질적으로 기존의 언어 지식을 강화하기보다 특정 과제에 최적화되기 때문일 가능성이 있습니다.\"]},{t:\"h3\",a:{id:\"section-5\"},c:[\"3. Multilinguality at Scale\"]},{t:\"p\",a:{},c:[\"KMMLU 평가 결과는 대규모 모델이 Curse of Multilinguality 문제(다국어 데이터로 학습할 경우, 모델의 성능이 떨어지는 문제)를 극복할 수 있음을 보여줍니다. 예를 들어, Llama-2, Yi, Qwen과 같은 다국어 모델은 한국어 데이터의 비중이 낮음에도 불구하고 한국어 특화 언어 모델인 Polyglot-Ko보다 뛰어난 성능을 보였습니다. 이는 대규모 데이터 학습과 모델 크기의 확대가 한국어와 같은 비주류 언어에서도 긍정적인 성능 전이를 가능하게 한다는 점을 나타냅니다.\"]},{t:\"h3\",a:{id:\"section-6\"},c:[\"4. Continual Pretraining\"]},{t:\"p\",a:{},c:[\"Yi 시리즈 모델(Yi-Ko-6B, Yi-Ko-34B)은 기존의 영어-중국어 이중언어 모델에서 추가적인 한국어 학습(Continual Pretraining)을 통해 성능이 크게 향상되었습니다. 특히 Yi-Ko-34B 모델은 60억 개 이상의 추가 토큰 학습 후 평균 50.46%의 성능을 기록하였으며, 이는 Polyglot-Ko보다 월등히 높은 수준입니다. 이 결과는 특정 언어에 대한 Continual Pretraining이 모델의 특정 언어에 대한 이해도를 강화하는 효과적인 전략임을 시사합니다.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/4YLi9qlzhBWUVJp19N6jAqVloQA.png?width=1920&height=1080\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-7\"},c:[\"KMMLU가 제시하는 한국어 AI의 미래\"]},{t:\"p\",a:{},c:[\"KMMLU 벤치마크의 도입은 한국어 대형 언어 모델 개발의 중요한 이정표를 제시합니다. 기존의 번역 기반 평가와는 달리, 한국 문화와 언어적 뉘앙스를 충실히 반영한 이 데이터셋은 언어 모델의 한계를 명확히 보여주었으며, 새로운 한국어 벤치마크 제작에 방향을 제시하고 있습니다. 특히, 최신 LLM조차도 한국어에서 기대 이하의 성과를 보였다는 점은 한국어 특화 벤치마크 뿐만 아니라, 모델 개발의 필요성을 다시 한번 강조합니다.\"]},{t:\"p\",a:{},c:[\"이 블로그를 마무리하며, KMMLU는 단순히 평가 도구를 넘어, 언어 모델 연구와 실질적인 활용 간의 간극을 좁히는 데 중요한 발판이 될 것임을 기대해 봅니다. 앞으로 더 정교하고 한국어 친화적인 AI 모델이 개발되어 우리 삶에 실질적인 도움을 주는 날을 고대하며, 이번 논문 리뷰를 마칩니다.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h6\",a:{},c:[{t:\"strong\",a:{},c:[\"Reference\"]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"📝 Paper\"]},\": \",{t:\"a\",a:{href:\"https://arxiv.org/abs/2402.11548\"},c:[\"KMMLU: Measuring Massive Multitask Language Understanding in Korean\"]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"🖥 \",{t:\"strong\",a:{},c:[\"Dataset (KMMLU)\"]},\": \",{t:\"a\",a:{href:\"https://huggingface.co/datasets/HAERAE-HUB/KMMLU\"},c:[\"HAERAE-HUB/KMMLU\"]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"🖥 \",{t:\"strong\",a:{},c:[\"Dataset (KMMLU-HARD)\"]},\": \",{t:\"a\",a:{href:\"https://huggingface.co/datasets/HAERAE-HUB/KMMLU-HARD\"},c:[\"HAERAE-HUB/KMMLU-HARD\"]}]}]}]}],toc:[{id:\"section-1\",text:\"다국어 벤치마크의 한계와 KMMLU의 등장\"},{id:\"section-2\",text:\"KMMLU: 한국어 벤치마크의 새로운 지평을 열다\"},{id:\"section-3\",text:\"KMMLU 실험 결과 분석\"},{id:\"section-4\",text:\"2. Fine-Tuning\"},{id:\"section-5\",text:\"3. Multilinguality at Scale\"},{id:\"section-6\",text:\"4. Continual Pretraining\"},{id:\"section-7\",text:\"KMMLU가 제시하는 한국어 AI의 미래\"}],images:[\"https://framerusercontent.com/images/btDwW2OSnF8GM4yO5kbb9cwzlXw.png?width=1920&height=434\",\"https://framerusercontent.com/images/vLCWtzNDxWVamhf62TcGtqOxAw.png?width=1920&height=952\",\"https://framerusercontent.com/images/AdK3OR8CQ8Cu2tZNRp7RkgoM.png?width=1920&height=612\",\"https://framerusercontent.com/images/95EtWE6lWRhpN30TjsQ9C8BNc0.png?width=1920&height=668\",\"https://framerusercontent.com/images/4YLi9qlzhBWUVJp19N6jAqVloQA.png?width=1920&height=1080\"],readMins:9},{id:27,page:\"Journal 28\",slug:\"multi-task-inference\",kind:\"research\",topic:\"Evaluation\",title:\"MTI Bench로 본 LLM의 Multi-Task Inference 능력\",originalTitle:\"MTI Bench로 본 LLM의 Multi-Task Inference 능력\",summary:\"다중 과제를 함께 처리하는 모델의 추론 능력 평가\",language:\"ko\",date:\"Jan 9, 2025\",dateISO:\"2025-01-09\",source:\"https://onelineai.com/en/blog-research/mti-bench\",path:\"/research/multi-task-inference\",nodes:[{t:\"blockquote\",a:{},c:[{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:['\"',{t:\"a\",a:{href:\"https://arxiv.org/abs/2402.11597\"},c:[\"Multi-Task Inference: Can Large Language Models Follow Multiple Instructions at Once?\"]},'\" 논문을 기반으로 LLM의 다중 과제 추론 방법론인 ',{t:\"strong\",a:{},c:[\"Multi-Task Inference\"]},\"와 LLM의 다중 추론 능력을 평가하는 \",{t:\"strong\",a:{},c:[\"MTI Bench\"]},\"에 관한 내용을 다루고 있습니다. 해당 논문은 \",{t:\"a\",a:{href:\"https://2024.aclweb.org/\"},c:[\"ACL 2024 \"]},\"학회에 게재되었습니다.\"]}]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"이 시리즈는 원라인에이아이 AI에서 발표한 논문을 리뷰합니다. 논문과 관련해서 궁금한 내용이 있다면 \",{t:\"a\",a:{href:\"https://www.onelineai.com/contact\"},c:[{t:\"strong\",a:{},c:[\"원라인에이아이 AI팀\"]}]},\"에게 문의주시기 바랍니다.\"]}]}]}]},{t:\"h2\",a:{id:\"section-1\"},c:[\"거대 언어 모델 (LLM)은 다중 과제를 동시에 처리할 수 있을까?\"]},{t:\"p\",a:{},c:[\"최근 몇 년간 거대 언어 모델 (LLM)은 자연어 처리 (NLP) 분야에서 획기적인 발전을 이루며 다양한 과제를 수행하는 데 있어 놀라운 성능을 보여주고 있습니다. LLM은 단순한 텍스트 생성이나 번역을 넘어, 복잡한 명령을 이해하고 이를 기반으로 추론하는 데까지 발전하였으며, OpenAI의 GPT 및 o1 모델 시리즈와 Meta의 Llama 모델 시리즈 같은 대표적인 사례는, 특정한 지침을 따르는 작업에서 매우 우수한 성능을 발휘하며 사용자의 요구를 충족시키는 데 성공했습니다.\"]},{t:\"p\",a:{},c:[\"하지만 이러한 모델들은 대개 한 번의 추론 호출에서 \",{t:\"strong\",a:{},c:[\"단일 명령\"]},\"만을 처리하도록 설계되었습니다. 즉, 각 작업은 독립적으로 수행되며, 여러 작업을 처리하려면 여러 번의 호출이 필요합니다. 이러한 한계는 모델의 활용도를 제한할 뿐만 아니라, 처리 시간이 증가하고 자원 효율성이 떨어지는 결과를 초래할 수 있습니다. 그렇다면, 하나의 추론 호출에서 \",{t:\"strong\",a:{},c:[\"다중 과제\"]},\" (Multi-Task)를 동시에 처리할 수 있다면 어떨까요? 이는 LLM의 효율성을 극대화하고, 더 복잡한 작업 환경에서도 활용할 수 있는 가능성을 열어줍니다.\"]},{t:\"p\",a:{},c:[\"“Multi-Task Inference: Can Large Language Models Follow Multiple Instructions at Once?” 논문에서는 모델의 multi-task 수행 능력을 평가하기 위해 \",{t:\"strong\",a:{},c:[\"MTI Bench\"]},\" (Multi-Task Inference Benchmark)라는 평가 벤치마크를 소개합니다. 이 문제들을 통해 LLM이 단일 호출로 여러 명령을 수행할 수 있는지, 그리고 성능과 속도에서 어떤 이점을 제공하는지를 중점적으로 분석합니다.\"]},{t:\"h2\",a:{id:\"section-2\"},c:[\"MTI Bench: LLM의 다중 작업 수행 능력을 측정하다\"]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"MTI Bench\"]},\"(Multi-Task Inference Benchmark)는 LLM의 \",{t:\"strong\",a:{},c:[\"다중 작업 수행\"]},\"(Multi-Task Inference) 능력을 테스트하기 위해 설계된 평가 벤치마크로, 25개의 task에 걸쳐서, 총 5,000개의 데이터 인스턴스로 구성되어 있습니다. 각 task는 2~3개의 하위 task로 구성되어 있어 모델이 task 간의 의존성을 어떻게 처리하는지 평가할 수 있습니다.\"]},{t:\"h4\",a:{},c:[\"[Task Formulation: MTI Bench의 구성 요소]\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/vVjfFYx2K3uHLKCuh3k7vQ.png?width=1920&height=636\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"MTI Bench는 위 그림과 같이 각 task의 정확성과 중간 수행 단계를 평가하기 위한 \",{t:\"strong\",a:{},c:[\"Multi-Step\"]},\" & \",{t:\"strong\",a:{},c:[\"Multi-Part\"]},\" 두 가지 주요 하위 집합으로 구성되어 있습니다:\"]},{t:\"ol\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Multi-Step\"]},\"(이미지 왼쪽): 순차적으로 \",{t:\"strong\",a:{},c:[\"상호 의존적인 작업\"]},\"들을 다루며 모델이 상호 의존적인 task를 올바르게 수행하는가를 측정합니다. 즉, 이전 작업의 답변이 추후의 작업에 사용되면서 최종적으로 올바른 정답을 도출하기 위한 중간 수행 단계의 정확도를 평가하며, 모델의 multi-step 추론 과정을 중점적으로 평가합니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Multi-Part\"]},\"(이미지 오른쪽): 작업 간 의존성이 없는 \",{t:\"strong\",a:{},c:[\"상호 독립적인 작업\"]},\"들을 다루며 모델이 상호 독립적인 task를 올바르게 수행하는가를 측정합니다. 즉, 문맥적으로 어느 정도 상통하지만, 완전히 독립적인 task를 모델이 능숙하게 수행할 수 있는가를 평가합니다.\"]}]}]},{t:\"h4\",a:{},c:[\"[Dataset Construction: MTI Bench는 어떻게 만들어졌는가?]\"]},{t:\"p\",a:{},c:[\"MTI Bench의 데이터셋은 기존 NLP 벤치마크로부터 다양한 작업을 선별해 조합함으로써 제작되었습니다. 데이터셋 구축 과정의 주요 단계는 다음과 같습니다:\"]},{t:\"ol\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Task source 선정\"]},\": 주요 데이터셋으로는 Quoref, SNLI, MMLU, MATH 등이 사용되었습니다. 선택 기준은 (1) \",{t:\"em\",a:{},c:[\"데이터의 품질 관리 프로세스가 잘 정의되어 있는가?\"]},\" (2) \",{t:\"em\",a:{},c:[\"더 복잡한 작업으로 통합할 가능성이 있는가?\"]},\" 를 기준으로 선정하였습니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Task 조합과 필터링\"]},\": 데이터셋은 2개의 그룹으로 나뉘어 task를 조합한 후, 낮은 품질이거나 정보성이 떨어지는 조합은 제거했습니다. 초기 32개의 작업 조합 중 7개가 제외되어, 최종적으로 \",{t:\"strong\",a:{},c:[\"25개의 고품질 task\"]},\"가 선정되었습니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"샘플링 및 데모 작성\"]},\": 각 task에 대해 Chain-of-Thought 방식으로 하위 task를 순차적으로 해결하는 데모를 생성하였습니다.\"]}]}]},{t:\"h2\",a:{id:\"section-3\"},c:[\"LLM의 다중 작업 추론 능력 평가\"]},{t:\"h4\",a:{},c:[\"[Baseline Inference Methods]\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/RMpBozTySqqIWfNJ2KgOx7jQ.png?width=1920&height=868\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"MTI Bench 평가 결과 비교를 위한 baseline으로 다음과 같이 3가지 평가 방식을 고려하였습니다. 위 이미지는 3개의 하위 task를 포함하고 있는 2개의 task에 대한 각 평가 방식의 예시입니다:\"]},{t:\"ol\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Single-Task Inference (이미지 상단)\"]},\": 하위 작업을 하나씩 순차적으로 수행하는 방식. 각 하위 task에 대해 모두 일일이 처리해야 하므로 총 \",{t:\"strong\",a:{},c:[\"6번\"]},\"의 추론 과정이 필요합니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Batch Prompting (이미지 중단)\"]},\": 동일한 하위 작업을 묶어 한 번에 처리하는 방식. 하위 task끼리 묶어서 추론하므로 총 \",{t:\"strong\",a:{},c:[\"3번\"]},\"의 추론 과정이 필요합니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Multi-Task Inference (이미지 하단)\"]},\": 논문에서 제안하는 방식으로, 하나의 프롬프트에 여러 하위 작업을 포함하여 한 번에 처리하는 방식. 각 하위 task를 모두 묶어서 큰 task 하나를 추론하므로 총 \",{t:\"strong\",a:{},c:[\"2번\"]},\"의 추론 과정이 필요합니다.\"]}]}]},{t:\"h4\",a:{},c:[\"[Experiment Results]\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/1IktBjRcNV3XFr8mDMbyGpWt1o.png?width=1838&height=754\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"ol\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Multi-Task Inference의 성능\"]},\": Multi-Task Inference는 Single-Task Inference 및 Batch Prompting과 비교했을 때 \",{t:\"strong\",a:{},c:[\"일관되게 높은 성능\"]},\"을 보여주었습니다. 또한 실험 결과는 모델의 크기와 성능 간에 강한 상관관계를 보이며 모델의 크기가 커질수록 강력한 Multi-Task Inference 성능을 보여줬습니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Multi-Step vs. Multi-Part 작업 종속성 영향\"]},\": Multi-Step의 경우 모델이 상호 의존적인 문제에 대해 순차적으로 문제 해결을 해야 하므로 성능이 전반적으로 떨어집니다. 반면에 Multi-Part는 작업 간에 의존성이 없으므로 더 높은 성능을 보이는 것을 확인할 수 있습니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Multi-Task Inference와 Single-Task Inference의 차이 분석\"]},\": Multi-Task Inference는 Single-Task Inference에 비해 평균적으로 7.3~12.4% 높은 정확도를 보여주는데, 이는 하위 task 간의 연관성이 높은 Multi-Step에서는 크게 나타나지만 하위 task 간의 연관성이 낮은 Multi-Part에서는 상대적으로 성능 차이가 작게 나타나는 것을 확인할 수 있습니다. Single-Task Inference는 첫 번째 task만을 해결한 후 다음 task로 넘어가는 방식인 반면, Multi-Task Inference는 모든 task의 정보를 한 번에 제공하여, 이로 인해 다음 task의 정보를 미리 참조 (look-ahead)하여 초기 작업을 더 잘 수행하는 경향이 다음의 표를 통해 확인되었습니다.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/946fHEtunhYCInH1S28cXPmZE.png?width=1920&height=540\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Multi-Task Inference의 효율성\"]},\": Multi-Task Inference는 Single-Task Inference 대비 평균적으로 \",{t:\"strong\",a:{},c:[\"1.46배 더 빠른 처리 속도\"]},\"를 기록했습니다. 이는 하위 task마다 별도의 추론 단계를 거치지 않고 한 번의 프롬프트로 작업을 처리하기 때문입니다. 반면에 Batch Prompting은 Single-Task Inference 대비 평균적으로 2.1배 더 빠른 처리 속도를 기록하며 가장 빠른 추론 속도를 보였지만, Multi-Task Inference에 비해 현저히 떨어지는 다중 작업 처리 능력을 보여주면서 Multi-Task Inference의 성능 및 효율성을 입증하였습니다.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/swRGiNsG0rDMdDypfK6p0bGep8.png?width=1920&height=400\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]}]}]},{t:\"h2\",a:{id:\"section-4\"},c:[\"Multi-Task 추론 분석: LLM의 새로운 가능성\"]},{t:\"p\",a:{},c:[\"현대의 대규모 언어 모델(LLMs)은 단일 지시문을 기반으로 작업을 수행하는 데 강점을 보여왔습니다. 하지만 여러 개의 지시문을 한 번에 처리하는 Multi-Task Inference에 대한 가능성은 여전히 미지수로 남아 있었습니다. “Multi-Task Inference: Can Large Language Models Follow Multiple Instructions at Once?” 논문에서는 이 질문을 중심으로 LLM이 동시에 여러 지시문을 효과적으로 처리할 수 있는지, 그리고 이러한 접근법이 어떤 이점을 제공하는지를 심도 있게 분석하였습니다.\"]},{t:\"p\",a:{},c:[\"LLM의 다중 작업 추론 능력을 평가하기 위해 제안된 MTI Bench에서의 평가 결과는 Multi-Task Inference 방식이 성능 형상, 추론 시간 단축 등의 이점을 보여주었습니다. 이러한 결과는 LLM이 단일 작업 수행을 넘어 다중 과제를 동시에 처리할 수 있는 잠재력을 시사합니다. 다중 과제 추론은 성능, 효율성, 그리고 모델의 응용 가능성 측면에서 새로운 기준을 제시하며 향후 AI 연구와 활용의 새로운 가능성을 제시합니다.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"Reference\"]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"📝 \",{t:\"strong\",a:{},c:[\"Paper\"]},\": \",{t:\"a\",a:{href:\"https://arxiv.org/abs/2402.11597\"},c:[\"Multi-Task Inference: Can Large Language Models Follow Multiple Instructions at Once?\"]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"🖥️ \",{t:\"strong\",a:{},c:[\"Code\"]},\": \",{t:\"a\",a:{href:\"https://github.com/guijinSON/MTI-Bench\"},c:[\"guijinSON/MTI-Bench\"]}]}]}]}],toc:[{id:\"section-1\",text:\"거대 언어 모델 (LLM)은 다중 과제를 동시에 처리할 수 있을까?\"},{id:\"section-2\",text:\"MTI Bench: LLM의 다중 작업 수행 능력을 측정하다\"},{id:\"section-3\",text:\"LLM의 다중 작업 추론 능력 평가\"},{id:\"section-4\",text:\"Multi-Task 추론 분석: LLM의 새로운 가능성\"}],images:[\"https://framerusercontent.com/images/vVjfFYx2K3uHLKCuh3k7vQ.png?width=1920&height=636\",\"https://framerusercontent.com/images/RMpBozTySqqIWfNJ2KgOx7jQ.png?width=1920&height=868\",\"https://framerusercontent.com/images/1IktBjRcNV3XFr8mDMbyGpWt1o.png?width=1838&height=754\",\"https://framerusercontent.com/images/946fHEtunhYCInH1S28cXPmZE.png?width=1920&height=540\",\"https://framerusercontent.com/images/swRGiNsG0rDMdDypfK6p0bGep8.png?width=1920&height=400\"],readMins:8},{id:28,page:\"Journal 29\",slug:\"k2-eval\",kind:\"research\",topic:\"Evaluation\",title:\"K2-Eval: 한국어와 한국 문화에 대한 평가\",originalTitle:\"K2-Eval: 한국어와 한국 문화에 대한 평가\",summary:\"한국어 및 한국 문화에 대한 모델 평가\",language:\"ko\",date:\"Jan 29, 2025\",dateISO:\"2025-01-29\",source:\"https://onelineai.com/en/blog-research/k2-eval\",path:\"/research/k2-eval\",nodes:[{t:\"blockquote\",a:{},c:[{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:['\"K2-Eval: Harnessing the Evaluation of Linguistic Fluency and Ethnolinguistic Knowledge in Korean\" 논문을 기반으로 LLM의 한국어 및 한국 문화 이해 능력과 지식을 평가하는 ',{t:\"strong\",a:{},c:[\"K2-Eval\"]},\"에 관한 내용을 다루고 있습니다. 해당 논문은 \",{t:\"a\",a:{href:\"https://sites.google.com/view/c3nlp\"},c:[\"C3NLP @ ACL 2024\"]},{t:\"a\",a:{href:\"https://2024.aclweb.org/\"},c:[\" \"]},\"학회에 게재되었습니다.\"]}]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"이 시리즈는 원라인에이아이 AI에서 발표한 논문을 리뷰합니다. 논문과 관련해서 궁금한 내용이 있다면 \",{t:\"a\",a:{href:\"https://www.onelineai.com/contact\"},c:[{t:\"strong\",a:{},c:[\"원라인에이아이 AI팀\"]}]},\"에게 문의주시기 바랍니다.\"]}]}]}]},{t:\"h2\",a:{id:\"section-1\"},c:[{t:\"strong\",a:{},c:[\"언어적 & 문화적 모델 평가\"]}]},{t:\"p\",a:{},c:[\"현대 인공지능 기술이 점차 발전하면서, 다양한 언어와 문화적 맥락을 이해하고 처리할 수 있는 거대 언어 모델 (LLM)의 중요성이 강조되고 있습니다. 하지만 지식 평가, 언어 번역, 텍스트 생성 능력만으로는 언어를 온전히 이해하고 활용한다고 보기 어렵습니다. 특정 언어를 이해한다는 것은 해당 언어를 사용하는 국가의 전통, 풍습, 문화, 견해 등을 포괄적으로 이해하고 활용한다는 것이기 때문입니다.\"]},{t:\"p\",a:{},c:[\"이러한 배경에서 등장한 \",{t:\"strong\",a:{},c:[\"K2-Eval\"]},\"은 LLM의 언어적 유창성뿐만 아니라, 한국어를 사용하는 지역의 문화적 맥락과 언어적 지식을 얼마나 잘 반영하는지를 평가합니다. K2-Eval은 90개의 사람이 직접 작성한 지시문을 통해 한국 문화와 언어적 맥락을 깊이 이해해야만 정확히 해결할 수 있는 문제를 제시하며, 이를 통해 한국어에 특화된 언어 모델의 성능을 검증할 수 있도록 고안되었습니다.\"]},{t:\"h2\",a:{id:\"section-2\"},c:[{t:\"strong\",a:{},c:[\"K2-Eval: Korean Language & Korean Culture Evaluation\"]}]},{t:\"p\",a:{},c:[\"K2-Eval은 다른 Language Fluency 벤치마크들과 다음과 같은 차이점을 지닙니다. 영어권에서 많이 사용되는 MT-Bench와 Vicuna Instructions 80은 영어 벤치마크로 한국과 관련 없는 내용들로 구성되어 있습니다. 한국어에 대한 Language Fluency 평가를 위해 개발된 \",{t:\"a\",a:{href:\"https://huggingface.co/datasets/maywell/LogicKor\"},c:[\"LogicKor\"]},\"도 한국어로는 구성되어 있으나, 한국의 문화권과 관련된 내용으로 구성되어 있지는 않습니다. 반면에 K2-Eval은 한국어로 작성되고 한국 문화와 관련된 내용의 문제들로 구성되어 있어 언어 및 문화 양면에서 종합적으로 모델을 평가할 수 있습니다.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/R5X7GhQnXwhIoAmmFt2rkMjIHw.png?width=1920&height=464\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"K2-Eval은 다음의 표와 같이 9개의 Korean knowledge와 7개의 Reasoning capability에 걸친 문제로 구성되어 있으며, 한 가지 도메인에 집중하지 않고 다양한 주제와 추론 능력을 평가할 수 있도록 제작되었습니다. 각 문제에 대한 ground-truth 정답은 GPT-4와 Web search를 동반하여 제작되었으며, 모든 문제들은 정확도를 위해 human review까지 함께 진행하였습니다.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/CTs8KLKXcNQNZ5nEfNNYoPIlM0.png?width=1920&height=708\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"K2-Eval을 활용하여 30개의 open & closed-source 언어 모델에 대해 한국어 및 한국 문화 평가를 진행하였습니다. K2-Eval의 평가는 여타 다른 Language Fluency 벤치마크들과 마찬가지로 GPT-4를 LLM-as-a-judge로 활용하는 방식으로 평가할 수 있습니다. 하지만 보다 정확한 평가를 위해 약 5,400개의 모델 응답 결과를 human evaluator들이 직접 평가하였습니다. 모델 응답은 평가 기준에 따라 1-5 Likert scale로 평가되며, human evaluator의 편향을 방지하기 위해 하나의 모델 응답에 대해 서로 다른 평가자가 평가하여 두 annotator 간의 평균을 최종 스코어로 계산하였습니다. 최종 평가 결과는 다음의 그림과 같습니다.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/G0hOM8F2pa679tXO0pjJ2MZy4.png?width=1920&height=1080\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"평가 결과를 보면 한국어 데이터에서의 Instruction-tuning이 K2-Eval에서 효과적으로 작용한다는 것을 확인할 수 있습니다. SOLAR-10.7B-Instruct를 base model로 하는 EEVE-Korean-Instruct-10.8B와 KULLM3는 Command-R-Plus-104B와 Mixtral-8X22B-Instruct보다 훨씬 작은 사이즈임에도 불구하고 한국어 Insturction-tuning을 통해 비슷한 수준의 성능을 보여줍니다. 이는 한국어 Instruction-tuning이 확실히 사람의 선호도를 향상시킨다는 것을 시사합니다.\"]},{t:\"h2\",a:{id:\"section-3\"},c:[{t:\"strong\",a:{},c:[\"GPT-4, Human evlauator를 대체할 수 있을까?\"]}]},{t:\"p\",a:{},c:[\"대다수의 Language Fluency 평가 벤치마크는 평가의 용이함을 위해 human evaluator 대신 GPT-4와 같은 강력한 LLM을 평가자로 활용하는 LLM-as-a-Judge로 활용합니다. 하지만, GPT-4를 활용한 평가가 정말 human evaluator를 완벽하게 대체할 수 있을까요? 이를 분석하기 위해 K2-Eval에서의 사람 평가 결과와 GPT-4 평가 결과 간의 상관성을 분석하였습니다.\"]},{t:\"p\",a:{},c:[\"두 평가 결과 간의 상관성 분석 결과 0.86으로 human evaluator와 GPT-4는 높은 평가 상관성을 가지는 것을 확인하였습니다. 하지만, 다음의 표와 같이 모델 랭킹에 대해서는 큰 차이가 발생하는 것을 확인할 수 있는데, 이는 GPT-4가 human evaluator와 유사하게 socring을 하긴 하지만, 모델 간의 미묘한 차이를 파악하는데에는 어려움이 있다는 것을 보여줍니다.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/EH9WstRgBwamizWURFlg5qjDxg.png?width=1920&height=752\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"Reference\"]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"🤗 \",{t:\"strong\",a:{},c:[\"Dataset\"]},\": \",{t:\"a\",a:{href:\"https://huggingface.co/datasets/HAERAE-HUB/K2-Eval\"},c:[\"https://huggingface.co/datasets/HAERAE-HUB/K2-Eval\"]}]}]}]}],toc:[{id:\"section-1\",text:\"언어적 & 문화적 모델 평가\"},{id:\"section-2\",text:\"K2-Eval: Korean Language & Korean Culture Evaluation\"},{id:\"section-3\",text:\"GPT-4, Human evlauator를 대체할 수 있을까?\"}],images:[\"https://framerusercontent.com/images/R5X7GhQnXwhIoAmmFt2rkMjIHw.png?width=1920&height=464\",\"https://framerusercontent.com/images/CTs8KLKXcNQNZ5nEfNNYoPIlM0.png?width=1920&height=708\",\"https://framerusercontent.com/images/G0hOM8F2pa679tXO0pjJ2MZy4.png?width=1920&height=1080\",\"https://framerusercontent.com/images/EH9WstRgBwamizWURFlg5qjDxg.png?width=1920&height=752\"],readMins:4},{id:29,page:\"Journal 30\",slug:\"model-evaluation\",kind:\"research\",topic:\"Evaluation\",title:\"AI의 평가 능력, 정말 믿을 수 있을까? LLM-as-a-Judge와 Reward Model의 실험적 고찰\",originalTitle:\"AI의 평가 능력, 정말 믿을 수 있을까? LLM-as-a-Judge와 Reward Model의 실험적 고찰\",summary:\"LLM-as-a-Judge와 Reward Model의 평가 능력 분석\",language:\"ko\",date:\"Jan 30, 2025\",dateISO:\"2025-01-30\",source:\"https://onelineai.com/en/blog-research/llm-as-a-judge-reward-models\",path:\"/research/model-evaluation\",nodes:[{t:\"blockquote\",a:{},c:[{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:['\"',{t:\"a\",a:{href:\"https://arxiv.org/abs/2409.11239\"},c:[\"LLM-as-a-Judge & Reward Model: What They Can and Cannot Do\"]},'\" 논문을 기반으로 LLM의 한국어 및 한국 문화 이해 능력과 지식을 평가하는 ',{t:\"strong\",a:{},c:[\"K2-Eval\"]},\"에 관한 내용을 다루고 있습니다. 해당 논문은 \",{t:\"a\",a:{href:\"https://sites.google.com/servicenow.com/good-data-2025/call-for-papers\"},c:[\"GOOD-DATA @ AAAI 2025\"]},{t:\"a\",a:{href:\"https://2024.aclweb.org/\"},c:[\" \"]},\"학회에 게재되었습니다.\"]}]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"이 시리즈는 원라인에이아이 AI에서 발표한 논문을 리뷰합니다. 논문과 관련해서 궁금한 내용이 있다면 \",{t:\"a\",a:{href:\"https://www.onelineai.com/contact\"},c:[{t:\"strong\",a:{},c:[\"원라인에이아이 AI팀\"]}]},\"에게 문의주시기 바랍니다.\"]}]}]}]},{t:\"h2\",a:{id:\"section-1\"},c:[{t:\"strong\",a:{},c:[\"LLM-as-a-Judge & Reward Model, 신뢰해도 되는 걸까?\"]}]},{t:\"p\",a:{},c:[\"최근 AI 연구에서 LLM-as-a-Judge와 Reward Model이 사람 평가자를 대체하는 방법으로 주목받고 있습니다. 기존의 자동 평가 방법이 단순한 정답 여부 판단에 그쳤다면, LLM을 이용한 평가는 긴 서술형 답변의 품질을 정량화하고, 리더보드에서 모델의 성능을 측정하는 주요 도구로 활용됩니다. 특히 강화 학습을 통한 모델 정렬(Alignment)에서 중요한 역할을 하며, OpenAI의 ChatGPT나 Anthropic의 Claude 같은 대형 AI 시스템의 발전에도 영향을 미치고 있습니다.\"]},{t:\"p\",a:{},c:[\"하지만, 과연 LLM 기반 자동 평가 시스템이 완벽한 대안이 될 수 있을까요? ****OnelineAI 연구팀은 한국어와 영어로 이루어진 bilingual meta-evaluation 데이터셋인 \",{t:\"strong\",a:{},c:[\"KUDGE\"]},\"를 제안하며, LLM-as-a-Judge와 보상 모델이 가지는 가능성과 한계를 \",{t:\"strong\",a:{},c:[\"비영어권 언어(한국어) 평가, 사실 검증, 복잡한 추론 문제에서의 취약점\"]},\"을 집중적으로 탐구하였습니다. KUDGE 데이터셋에서의 분석 결과를 간략히 정리하면 다음의 그림과 같습니다:\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/2azj24HwaevQ4g4UofrqNFztDA.png?width=1920&height=544\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"영어 평가 능력이 다른 언어 평가에도 강한 영향을 미치며, \",{t:\"strong\",a:{},c:[\"영어에서 잘 작동하는 평가 모델이 한국어에서도 유사한 성능을 보이는 경향\"]},\"이 있음.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"하지만, \",{t:\"strong\",a:{},c:[\"사실 검증과 문화적 맥락에 대한 이해가 부족하여 잘못된 정보나 편향된 내용을 제대로 걸러내지 못하는 문제\"]},\"가 존재함.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"LLM 기반 평가 모델들은 단순한 문장 비교에는 강하지만, \",{t:\"strong\",a:{},c:[\"복잡한 논리적 추론이 필요한 질문에서는 제대로 평가하지 못하는 한계\"]},\"가 있음.\"]}]}]},{t:\"p\",a:{},c:[\"본 포스팅에서는 KUDGE 데이터셋과 함께 LLM-as-a-Judge와 Reward Model의 활용성에 대해 자세하게 알아보도록 하겠습니다.\"]},{t:\"h2\",a:{id:\"section-2\"},c:[{t:\"strong\",a:{},c:[\"KUDGE: 자동화 평가 방법의 평가를 위한 한국어 & 영어 벤치마크\"]}]},{t:\"p\",a:{},c:[\"KUDGE 벤치마크는 LLM-as-a-Judge와 Reward Model 같은 자동화된 평가 방법론이 모델의 응답을 정확하고 공정하게 평가할 수 있는지를 검증하기 위해 고안된 한국어 & 영어 bilingual 벤치마크 데이터셋입니다. 대부분의 한국어 Long-form Question Answering 벤치마크 데이터셋이 MT-Bench를 기반으로 번역되어 주로 영어에 초점이 맞춰져 있었던 반면, KUDGE는 한국어 평가의 정밀도를 높이고, AI 모델의 다국어 평가 능력을 분석하는 것을 목표로 합니다. 이 벤치마크는 다음과 같이 “Original”, “Challenge” 두 가지 서브셋으로 구성되어 있습니다.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"Original Subset (Korean Only)\"]}]},{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Original\"]},\" \",{t:\"strong\",a:{},c:[\"Subset\"]},\"은 9개의 한국 지식 카테고리와 7개의 추론 능력 카테고리에 걸친 90개의 직접 제작한 한국어 질문으로 구성되어 있는 한국의 문화적 요소를 반영한 서브셋입니다. 총 31개의 LLM을 활용하여 다양한 응답을 생성하고, 15명의 사람 평가자가 직접 평가한 레이블로 구성되어 있습니다. 해당 서브셋은 다음의 두 카테고리로 나뉩니다.\"]},{t:\"ol\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Pointwise Subset\"]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"단일 언어 모델의 응답에 대해 1~5점의 Likert Scale로 \",{t:\"strong\",a:{},c:[\"절대적 평가\"]},\"를 수행함.\"]}]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Pairwise Subset\"]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"두 언어 모델의 응답에 대해 \",{t:\"strong\",a:{},c:[\"더 나은 답변을 선택\"]},\"하여 \",{t:\"strong\",a:{},c:[\"상대적 평가\"]},\"를 수행함.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"서로 다른 모델에 대해 3점 이상을 받은 응답을 chosen, 2점 이하의 응답을 rejected로 선정함.\"]}]}]}]}]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"Challenge Subset (Korean & English)\"]}]},{t:\"p\",a:{},c:[\"Original Subset은 대부분 문화 관련 문제들로 구성되어 STEM 계열의 문제가 부족하다는 문제점을 가지고 있습니다. 이러한 문제를 해결하기 위해 한국어와 영어 bilingual 데이터로 구성되어 있는 \",{t:\"strong\",a:{},c:[\"Challenge Subset\"]},\"을 제작하였습니다.\"]},{t:\"ol\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Pairwise-Easy\"]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"MMLU의 \",{t:\"code\",a:{},c:[\"college physics\"]},\", \",{t:\"code\",a:{},c:[\"college mathematics\"]},\", \",{t:\"code\",a:{},c:[\"high school chemistry\"]},\", \",{t:\"code\",a:{},c:[\"high school geography\"]},\" 서브셋에서 추출된 문제로 구성.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"code\",a:{},c:[\"EXAONE-3.0-7.8B-Instruct\"]},\" 가 생성한 정답 & 오답을 각각 chosen & rejected response로 사용\"]}]}]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Pairwise-Hard\"]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"GPQA에서 추출된 문제로 구성.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"code\",a:{},c:[\"gpt-4o\"]},\" 가 생성한 정답 & 오답을 각각 chosen & rejected response로 사용\"]}]}]}]}]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h2\",a:{id:\"section-3\"},c:[{t:\"strong\",a:{},c:[\"KUDGE Experiments\"]}]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"KUDGE Original Subset 평가 결과\"]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/nVvOBBMHVdWzUAF6fJbxuSULLE.png?width=1920&height=944\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"20개 모델에 대한 KUDGE Original Subset 평가 결과는 위의 표와 같습니다. 평가 결과를 통해 다음과 같은 분석 결과를 얻었습니다.\"]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"상업용 모델(GPT-4o, Claude-3.5-Sonnet)과 큰 사이즈의 모델(Llama-3.1-405B-Instruct, Mistral-Large-Instruct)은 비슷하게 준수한 성능을 보였지만, Pointwise 카테고리에서 대부분의 모델이 0.6 이하의 Pearson Correlation을 보여주면서 아직 부족한 성능을 보였음.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"모델 사이즈가 성능에 중요한 영향을 미치는 것은 맞지만, 학습 데이터 퀄리티, 데이터셋 사이즈, 학습 비용 등의 요소도 중요하게 작용함.\"]}]}]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"평가 능력이 다른 언어로 전이될 수 있을까?\"]}]},{t:\"p\",a:{},c:[\"앞선 KUDGE Original Subset의 평가 결과를 보면, 대부분의 모델이 영어로 학습되었음에도 불구하고, 한국어 평가를 준수하게 진행하는 것을 확인할 수 있습니다. 이러한 관점에서 “\",{t:\"em\",a:{},c:[\"평가 능력이 다른 언어로도 전이될 수 있을까?\"]},\"”의 의문점을 해결하기 위해 언어 모델이 영어에서 학습한 평가 능력을 한국어와 같은 전혀 다른 언어에서도 발휘할 수 있는지를 실험했습니다. 실험을 위해 \",{t:\"a\",a:{href:\"https://arxiv.org/abs/2402.11548\"},c:[\"KMMLU\"]},\"를 활용하여 모델의 한국어 성능을 평가하였고, \",{t:\"a\",a:{href:\"https://arxiv.org/abs/2403.13787\"},c:[\"RewardBench\"]},\"를 활용하여 영어 평가 능력을 평가하여 KUDGE score 간의 regression을 계산하였습니다.\"]},{t:\"p\",a:{},c:[\"Regression 계산 결과 KMMLU 점수보다 Reward Bench 점수가 더 높은 R2 value를 보여주면서 한국어 능력이 부족함에도 불구하고 영어 평가를 더 잘하는 모델이 더 좋은 KUDGE 성능을 가진다는 것을 보여줍니다. (해당 결과는 아래의 그림을 통해 확인하실 수 있습니다.)\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/TuRvpLDlA4PU3nTagnhPa9BHEhA.png?width=1920&height=616\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"추가적인 분석을 위해 영어 데이터로 학습된 Mistral-7B & Mixtral-8x7B 모델과 해당 모델에 대해 영어 평가 능력을 학습시킨 Prometheus2-7B & Prometheus2-8x7B 모델의 KUDGE 성능을 분석하였습니다. 두 모델군 간에는 영어 평가 학습 여부의 차이밖에 없음에도 불구하고, KUDGE 성능과 correlation, Failure case가 크게 향상되는 것을 확인할 수 있습니다. 이는 평가 능력 학습이 영어로 진행되었음에도 불구하고, 다른 언어로 확장될 수 있다는 점을 시사합니다.\"]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/5GhwtHNpcpTqHbUxQ2Jzb6KHxo.png?width=1920&height=396\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"LLM이 거짓 정보를 감지할 수 있을까?\"]}]},{t:\"p\",a:{},c:[\"앞선 분석을 통해 영어로 학습된 모델의 평가 능력이 다른 언어로도 전이된다는 점을 확인하였지만, 거짓 정보를 탐지해낼 수 있는지에 대해서는 아직 불분명합니다. 이를 확인하기 위해 human annotator가 단어, 문장, 문단 단위로 거짓 정보를 삽입한 응답에 대해서 모델이 어떻게 평가하는가를 확인하였습니다. 평가는 \",{t:\"code\",a:{},c:[\"gpt-4o\"]},\" , \",{t:\"code\",a:{},c:[\"Claude-3.5-Sonnet\"]},\" 에 대해서 진행되었으며, Pointwise와 Pairwise 두 가지 방식으로 평가했습니다.\"]},{t:\"ol\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Pointwise 평가\"]},\": LLM이 개별 응답을 평가하고, 피드백을 생성하여 거짓 정보를 감지하는지 확인. 두 모델 모두 거짓 정보를 탐지하는데 어려움을 겪었고, 문단 수준의 오류는 상대적으로 감지가 쉬웠으나, 단어 수준의 작은 변경은 거의 감지하지 못했음.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Pairwise 평가\"]},\": 원본 응답과 거짓 정보를 포함한 응답을 비교하고 올바른 응답을 선택하는지 확인. Pointwise보다 전반적으로 나은 성능을 보여줬지만, 작은 사이즈의 Reward Model은 50% 이하의 정확도를 보여주며, 사실 확인 능력이 낮았음.\"]}]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/o3qyQGzYKgVY5wfrallDAr1FnY.png?width=1920&height=468\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"모델 평가 결과에 대한 분석은 다음과 같습니다.\"]},{t:\"ol\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"LLM의 거짓 정보 탐지 능력 부족\"]},\". 문장이나 문단에서는 어느 정도 정확도를 보여주지만, 단어 수준의 작은 변경은 거의 탐지하지 못했습니다. 이는 LLM이 ‘사실 검증’ 보다는 ‘논리적 일관성 평가’에 좀 더 초점을 맞추고 있기 때문으로 보입니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Pairwise 방식의 효과\"]},\". 원본 응답과 거짓 정보를 포함한 응답을 주고 Pairwise 평가를 진행하면 거짓 정보를 탐지하는 정확도가 늘어납니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Reward Model의 사실 검증 부적합\"]},\". Reward Model은 거짓 정보를 포함한 응답을 거의 구별하지 못하며, 이는 Reward Model이 주로 인간의 선호도를 학습하는데 최적화되어 있기 때문일 가능성이 큽니다.\"]}]}]},{t:\"h4\",a:{},c:[{t:\"strong\",a:{},c:[\"LLM이 고난도 문제에 대해서도 잘 평가할 수 있을까?\"]}]},{t:\"p\",a:{},c:[\"일반적으로 어떤 문제의 정답이 맞는지를 평가하려면 그 문제를 해결할 수 있어야 합니다. 이러한 직관적인 가정을 바탕으로, \",{t:\"strong\",a:{},c:[\"LLM이 어려운 질문을 스스로 해결하지 못한다면 이를 정확히 평가하는 것도 어려울 것\"]},\"이라고 가정하며, 이를 검증하기 위해 KUDGE Challenge 데이터셋을 활용하여, 문제의 난이도가 모델의 평가 능력에 어떤 영향을 미치는지를 실험했습니다.\"]},{t:\"p\",a:{},c:[\"실험 결과 모델의 평가 성능은 질문의 난이도에 따라 큰 차이를 보이는 것을 확인하였습니다.\"]},{t:\"ol\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Easy 카테고리의 비교적 높은 정확도\"]},\". 대부분 70% 이상의 성능을 보였으며, Reward Model도 LLM-as-a-Judge와 비슷한 수준의 성능을 보여줬음.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"Hard 카테고리의 큰 성능 저하\"]},\". 대부분의 모델이 낮은 성능을 보였고, 일부 Reward Model은 랜덤 추측(50%)보다 낮은 성능을 기록함.\"]}]}]},{t:\"img\",a:{src:\"https://framerusercontent.com/images/nIEsW1NOC8FZvBHK5917VlgUGGk.png?width=1920&height=640\",alt:\"Figure from the original publication\",loading:\"lazy\",decoding:\"async\"},c:[]},{t:\"p\",a:{},c:[\"결과에 대한 분석은 다음과 같습니다.\"]},{t:\"ol\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"모델 자체의 문제 해결 능력 부족\"]},\". 모델이 답을 평가하려면 우선 해당 문제를 해결할 수 있어야 하는데, 그러지 못하여 평가가 부정확해졌을 가능성이 있습니다.\"]}]},{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[{t:\"strong\",a:{},c:[\"모델 크기의 한계\"]},\". 대부분의 Reward Model 크기가 LLM-as-a-Judge보다 작아서 Hard 카테고리에서 낮은 성능을 보였을 수 있습니다\"]}]}]},{t:\"h2\",a:{id:\"section-4\"},c:[{t:\"strong\",a:{},c:[\"LLM-as-a-Judge & Reward Model의 향후 방향성\"]}]},{t:\"p\",a:{},c:[\"LLM-as-a-Judge & Reward Model의 역할은 LLM의 평가 및 보정에 있어 점점 더 중요해지고 있습니다. 본 연구는 이러한 자동화된 평가 모델이 갖는 한계를 명확히 보여줍니다.\"]},{t:\"p\",a:{},c:[\"주요한 발견 중 하나는 영어로 학습된 평가 모델이 한국어에서도 일정 수준의 평가 능력을 유지한다는 점입니다. 이는 평가 능력이 특정 언어에 종속되지 않고 어느 정도 언어 불문 공통적으로 작용할 수 있음을 시사합니다. 하지만 이러한 자동화된 평가 모델들은 사실 검증, 문화적 표현의 정확성, 그리고 복잡한 논리적 질문을 평가하는 데 있어서 심각한 한계를 드러냈습니다. 특히, 모델들이 거짓 정보를 탐지하고 이에 대한 적절한 패널티를 부여하는 능력이 매우 부족하다는 점은 우려할 만한 요소입니다.\"]},{t:\"p\",a:{},c:[\"또한, 최신의 강력한 LLM들도 난이도가 높은 질문(GPQA와 같은 문제)을 제대로 평가하지 못하는 것으로 나타났습니다. 이는 단순히 모델의 크기와 연산 능력의 문제라기보다는, 현재의 평가 시스템이 더 깊은 논리적 사고를 요하는 질문을 제대로 이해하고 판별하는 데 한계를 가지고 있기 때문입니다.\"]},{t:\"p\",a:{},c:[\"결론적으로, LLM을 평가하는 자동화된 방법론이 점점 발전하고 있지만, 여전히 인간 평가자의 역할을 완전히 대체하기에는 부족함이 많습니다. 향후 연구는 평가 모델이 보다 정밀하게 사실 검증을 수행하고, 문화적 맥락을 이해하며, 난이도 높은 질문을 공정하고 정확하게 평가할 수 있도록 발전하는 방향으로 나아가야 할 것입니다.\"]},{t:\"p\",a:{},c:[{t:\"br\",a:{},c:[]}]},{t:\"h5\",a:{},c:[{t:\"strong\",a:{},c:[\"Reference\"]}]},{t:\"ul\",a:{},c:[{t:\"li\",a:{},c:[{t:\"p\",a:{},c:[\"📝 \",{t:\"strong\",a:{},c:[\"Paper\"]},\": \",{t:\"a\",a:{href:\"https://arxiv.org/abs/2409.11239\"},c:[\"https://arxiv.org/abs/2409.11239\"]}]}]}]}],toc:[{id:\"section-1\",text:\"LLM-as-a-Judge & Reward Model, 신뢰해도 되는 걸까?\"},{id:\"section-2\",text:\"KUDGE: 자동화 평가 방법의 평가를 위한 한국어 & 영어 벤치마크\"},{id:\"section-3\",text:\"KUDGE Experiments\"},{id:\"section-4\",text:\"LLM-as-a-Judge & Reward Model의 향후 방향성\"}],images:[\"https://framerusercontent.com/images/2azj24HwaevQ4g4UofrqNFztDA.png?width=1920&height=544\",\"https://framerusercontent.com/images/nVvOBBMHVdWzUAF6fJbxuSULLE.png?width=1920&height=944\",\"https://framerusercontent.com/images/TuRvpLDlA4PU3nTagnhPa9BHEhA.png?width=1920&height=616\",\"https://framerusercontent.com/images/5GhwtHNpcpTqHbUxQ2Jzb6KHxo.png?width=1920&height=396\",\"https://framerusercontent.com/images/o3qyQGzYKgVY5wfrallDAr1FnY.png?width=1920&height=468\",\"https://framerusercontent.com/images/nIEsW1NOC8FZvBHK5917VlgUGGk.png?width=1920&height=640\"],readMins:10},{id:30,page:\"Journal 31\",slug:\"financial-conversations\",kind:\"stories\",topic:\"Conversation\",title:\"A more useful financial conversation\",originalTitle:\"OLA를 사용한 챗봇이 어떻게 사용자수를 280% 증가시켰을까요?\",summary:\"Connect financial knowledge to the questions people actually ask.\",language:\"en\",date:\"Dec 23, 2025\",dateISO:\"2025-12-23\",source:\"https://onelineai.com/en/blog-ola/aboutola1\",path:\"/stories/financial-conversations\",nodes:[{t:\"h2\",a:{id:\"section-1\"},c:[\"Start with a useful question\"]},{t:\"p\",a:{},c:[\"A financial assistant has to understand both the question and the domain behind it. The original OLA story describes moving beyond fixed responses toward conversations that retain context across follow-up questions.\"]},{t:\"h2\",a:{id:\"section-2\"},c:[\"Connect the context\"]},{t:\"p\",a:{},c:[\"Financial terminology, relevant knowledge and connected business information shape the response. The experience should help people move from an initial question to the information they need, with access boundaries defined by the organization.\"]},{t:\"h2\",a:{id:\"section-3\"},c:[\"Design for the person\"]},{t:\"p\",a:{},c:[\"Language and tone matter alongside the underlying model. Clear explanations and a natural conversational flow make an assistant easier to use. The workflow starts with an everyday task, then connects the knowledge needed to answer it.\"]},{t:\"h2\",a:{id:\"section-4\"},c:[\"From answer to work\"]},{t:\"p\",a:{},c:[\"OLA brings knowledge, models and agents into the workspace. When an agent proceeds from answering to taking action, the execution path can include the policies, approvals and evidence provided by RYNTA.\"]}],toc:[{id:\"section-1\",text:\"Start with a useful question\"},{id:\"section-2\",text:\"Connect the context\"},{id:\"section-3\",text:\"Design for the person\"},{id:\"section-4\",text:\"From answer to work\"}],images:[],readMins:2},{id:31,page:\"Journal 32\",slug:\"report-workflows\",kind:\"stories\",topic:\"Reporting\",title:\"From recurring data to a reviewable report\",originalTitle:\"리포트 생성, 이제 5초면 만들 수 있습니다.\",summary:\"Bring source data, a consistent format and human review into one reporting workflow.\",language:\"en\",date:\"Dec 25, 2025\",dateISO:\"2025-12-25\",source:\"https://onelineai.com/en/blog-ola/aboutola2\",path:\"/stories/report-workflows\",nodes:[{t:\"h2\",a:{id:\"section-1\"},c:[\"Bring the inputs together\"]},{t:\"p\",a:{},c:[\"Recurring reports depend on information arriving from different sources. The original OLA story describes a workflow that gathers connected data and prepares a structured draft around a recurring reporting task.\"]},{t:\"h2\",a:{id:\"section-2\"},c:[\"Keep a consistent structure\"]},{t:\"p\",a:{},c:[\"A defined report format gives the workflow a clear destination. The task is to bring relevant information into that structure, surface the points that need attention and connect the draft to its source material.\"]},{t:\"h2\",a:{id:\"section-3\"},c:[\"Leave room for judgment\"]},{t:\"p\",a:{},c:[\"A generated draft is a starting point for review. People interpret the information, check the supporting references and decide what is ready to share. The output should help the reviewer spend more time on meaning and less on assembling the document.\"]},{t:\"h2\",a:{id:\"section-4\"},c:[\"Fit the operating environment\"]},{t:\"p\",a:{},c:[\"Sources, schedules, permissions and review steps depend on the organization. Deployment is phased around the systems and security requirements involved; completion times are not universal.\"]}],toc:[{id:\"section-1\",text:\"Bring the inputs together\"},{id:\"section-2\",text:\"Keep a consistent structure\"},{id:\"section-3\",text:\"Leave room for judgment\"},{id:\"section-4\",text:\"Fit the operating environment\"}],images:[],readMins:2},{id:32,page:\"Journal 33\",slug:\"your-data-your-workspace\",kind:\"stories\",topic:\"Integration\",title:\"A workspace shaped around your data\",originalTitle:\"원하는 데이터를 골라서 우리 화사에 맞는 구조를 만들어보세요.\",summary:\"Choose the knowledge, systems and outputs that fit your organization.\",language:\"en\",date:\"Dec 28, 2025\",dateISO:\"2025-12-28\",source:\"https://onelineai.com/en/blog-ola/aboutola3\",path:\"/stories/your-data-your-workspace\",nodes:[{t:\"h2\",a:{id:\"section-1\"},c:[\"Choose the work first\"]},{t:\"p\",a:{},c:[\"Organizations begin with different documents, data sources and business systems. The original OLA story presents a modular approach: select the information, capabilities and output that a particular workflow needs.\"]},{t:\"h2\",a:{id:\"section-2\"},c:[\"Connect the right inputs\"]},{t:\"p\",a:{},c:[\"Internal knowledge and approved external data can play different roles. Define which information the workflow depends on and which people or teams should be able to use it.\"]},{t:\"h2\",a:{id:\"section-3\"},c:[\"Compose the workspace\"]},{t:\"p\",a:{},c:[\"Knowledge processing, model evaluation and agent workflows belong in a connected design. Existing systems and permissions shape how these pieces fit together, rather than requiring every team to adopt the same process.\"]},{t:\"h2\",a:{id:\"section-4\"},c:[\"Deliver a useful output\"]},{t:\"p\",a:{},c:[\"The result may be a report, an answer or a workflow step. Choose the output around the people who will use and review it. A phased rollout is tailored to the organization’s systems and security requirements.\"]}],toc:[{id:\"section-1\",text:\"Choose the work first\"},{id:\"section-2\",text:\"Connect the right inputs\"},{id:\"section-3\",text:\"Compose the workspace\"},{id:\"section-4\",text:\"Deliver a useful output\"}],images:[],readMins:2}];\nexport const __FramerMetadata__ = {\"exports\":{\"JOURNAL_2\":{\"type\":\"variable\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"__FramerMetadata__\":{\"type\":\"variable\"}}}\n//# sourceMappingURL=./GraphiteResearch2.map","// Same graphite assets, line-wrapped for editor stability\nexport const 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const __FramerMetadata__ = {\"exports\":{\"ART_1\":{\"type\":\"variable\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"__FramerMetadata__\":{\"type\":\"variable\"}}}\n//# sourceMappingURL=./GraphiteData1.map","// Same graphite assets, line-wrapped for editor stability\nexport const ART_2={agents:[\"data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAQDAwQDAwQEBAQFBQQFBwsHBwYGBw4KCggLEA4RERAOEA8SFBoWEhMYEw8QFh8XGBsbHR0dERYgIh8cIhocHRz/2wBDAQUFBQcGBw0HBw0cEhASHBwcHBwcHBwcHBwcHBwcHBwcHBwcHBwcHBwcHBwcHBwcHBwcHBwcHBwcHBwcHBwcHBz/wAARCAQABgADASIAAhEBAxEB/8QAHQAAAgEFAQEAAAAAAAAAAAAAAQIAAwQFBgcICf/EAFEQAAEDAwMCBAQEAgcHAgMCDwECAxEABCEFEjEGQRNRYXEHIoGRFDJCoSOxCBUzUmLB0RYkQ3KC4fBT8Rc0Y5KiJTVEVHOyJoPCGEVkdNLi/8QAGQEBAQEBAQEAAAAAAAAAAAAAAAECAwQF/8QAJhEBAQEAAgICAQQDAQEAAAAAAAERAjEDIRJBUQQTImEyQnGBFP/aAAwDAQACE\",\"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\",\"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\",\"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\",\"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\",\"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\",\"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\",\"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\",\"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\",\"kg5oDkzn70RjuB70k8+XvTCYwSB50Dbs9jSkweanHeJ79qAPMfzoASUmcn/ACqZg96c4kR+9BKSJzQCCB/3o5AnNRKgDB5qbgO80EKzI8veiZ8qABMwY+s1JGcxQBUkcRHrQkxiqgzzUIA8zQUionz9oo7tsCD9RTRuI86hJ4BoAZn/ADmpE4PaptPMxHc0JMYP3oG2gGTR2p5PelBI4ipJ4nNA0pE5mlkHIog4M0DHMR60EkDPFTcFcUCkTNAok80DeWcGiUxmD96EwfP0oyM4oIREnzpD82DmnAgmAfvSkDvx60AE/wDaoYUZ7+tEqA+nnRJkSKB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const __FramerMetadata__ = {\"exports\":{\"ART_2\":{\"type\":\"variable\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"__FramerMetadata__\":{\"type\":\"variable\"}}}\n//# sourceMappingURL=./GraphiteData2.map","// Same graphite assets, line-wrapped for editor stability\nexport const 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const __FramerMetadata__ = {\"exports\":{\"ART_3\":{\"type\":\"variable\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"__FramerMetadata__\":{\"type\":\"variable\"}}}\n//# sourceMappingURL=./GraphiteData3.map","// Same graphite assets, line-wrapped for editor stability\nexport const ART_4={approval:[\"data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAQDAwQDAwQEBAQFBQQFBwsHBwYGBw4KCggLEA4RERAOEA8SFBoWEhMYEw8QFh8XGBsbHR0dERYgIh8cIhocHRz/2wBDAQUFBQcGBw0HBw0cEhASHBwcHBwcHBwcHBwcHBwcHBwcHBwcHBwcHBwcHBwcHBwcHBwcHBwcHBwcHBwcHBwcHBz/wAARCAQABgADASIAAhEBAxEB/8QAHQAAAgIDAQEBAAAAAAAAAAAAAAECBAMFBgcICf/EAEwQAAIBAwMCBQIEBAMHAwECDwECAwAEEQUSITFBBhMiUWFxgQcUMpEjQqGxFVLBCCQzYnLR4UOC8BZTkvE0FyVjorJEwtImRVRzhP/EABkBAQEBAQEBAAAAAAAAAAAAAAABAgMEBf/EACcRAQEAAgICAgMAAgMBAQAAAAABAhEhMQNBElEEMmEicRNCUhSh/9oADAMBAAIRAxEAP\",\"wD7pxinRRXFsjT6HFGaPvQBNH3ozR3oHwKKVANAzRRRQGaeajRzQOil3p0BkU+Kj80f0oHRmiigKKRNHegCaM80E0qCWaAKVGaglmg0s80j14qgJo4FHeigCaVPig4FAvrTzSPNA6VAGnQMCg9aA5pZoozVBRRQOKgeaAc0qdA84pHrRnnmjrVCIxSzUjS4FA+adKnQLNH0ooNACnRSJoA0qeeaOtQI0sY+lM0UC57UA5p9qRqgzTHFLqKM9qgdHWlTzVDx3o4pA8080BQTmjNKgKdHaigD1o+9LNFQB60vrRmnVB060UUVAiaeeaO1HxVBRRg5oqAooooCijNGTQOkTRRVCzTzSzzQTQMmlRmg1A6KQ470ZzQOjrSozigZpA1hubqG0jMs8qxxjuxxXJ3/AI0a4LwaTEzyA48xlB/YZ/qf2osjtQahLcRQKXmlSNB/M7BR/WvMZZNf1MSeZqE8MWcYiIJByeN3Cj/tU4/DwkZ98ck8rZzLMxlI69zwK\",\"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\",\"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\",\"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\",\"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\",\"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\",\"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\",\"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\",\"4pdLyzjzLeOZyJI4yOJ8b931Tkk471lluEhlYR3lqxC+qGXcC3U556NWEaKI1DxbfOB3kOuQPcbhwB89awzWyXji32mFXBSSZX83C5J2j6+/apwvK7+chQrqCyM5uF2Dy8yADvgDt75q5aa1bz5jZnRmyBIVIB69+x45qha20GlrtRZPyw9PmJLnBGeMdRnv8ms0eHkknBCyt6Cjc8dAp5+5+1Sjau88TqYsTR4xtz6vt71miu0mLKQVZTgqf7/AEqhH5looQRvLCTnGcFevI55qy7LIAQxUdVOMj6H2rKrYXbkqMA+xyKQwwKsQT3BFYN6DGAVY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const __FramerMetadata__ = {\"exports\":{\"ART_4\":{\"type\":\"variable\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"__FramerMetadata__\":{\"type\":\"variable\"}}}\n//# sourceMappingURL=./GraphiteData4.map","// Same graphite assets, line-wrapped for editor stability\nexport const 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const __FramerMetadata__ = {\"exports\":{\"ART_5\":{\"type\":\"variable\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"__FramerMetadata__\":{\"type\":\"variable\"}}}\n//# sourceMappingURL=./GraphiteData5.map","// Same graphite assets, line-wrapped for editor stability\nexport const ART_6={compliance:[\"data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAQDAwQDAwQEBAQFBQQFBwsHBwYGBw4KCggLEA4RERAOEA8SFBoWEhMYEw8QFh8XGBsbHR0dERYgIh8cIhocHRz/2wBDAQUFBQcGBw0HBw0cEhASHBwcHBwcHBwcHBwcHBwcHBwcHBwcHBwcHBwcHBwcHBwcHBwcHBwcHBwcHBwcHBwcHBz/wAARCAQABgADASIAAhEBAxEB/8QAHQAAAQUBAQEBAAAAAAAAAAAAAQACAwQFBgcICf/EAFEQAAEDAwMBBgQDBQQJAwIADwECAxEABCEFEjFBBhMiUWFxMoGRoRRCsQcVI1LBYtHh8BYkM0NygpKi8VNjsgg0cyVEVBeDwmR0k9ImRbPi/8QAGQEBAQEBAQEAAAAAAAAAAAAAAAECAwQF/8QAJREBAQEAAQQCAgMBAQEAAAAAAAERAgMSITFBURNhBCJxgTKh/9oADAMBAAIRA\",\"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\",\"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\",\"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\",\"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\",\"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\",\"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\",\"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\",\"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\",\"c80lE+1IAn1qoMEjFL1JpbSDH9aBBH5ooCfT60toM5oST8qHJniiiMZp2QabjrzSx51A+Z4FAjz5pSCCKEc4qoXFLnrQI4PSiRINACMRQJjHWl88+RpTNFEHmeKJ6UgROBn1pH6UA9MzSn60ifPilJHXFAQR7f1ok9IpAgiAc0DPnmiB160uOn1NHI6zQIgTPyopSCDQyYxSkHNCImeKgdxxSB8hS980SogxVQ3k+VGDNKMwRRIgESaiiIPNAwOtLYTnkUog+dUESKMT60MHFGNvU0QDj0pCfPFEH5mmwOJNAs5j9aBnJmkT1nFL/M0CKjgcUR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const __FramerMetadata__ = {\"exports\":{\"ART_6\":{\"type\":\"variable\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"__FramerMetadata__\":{\"type\":\"variable\"}}}\n//# sourceMappingURL=./GraphiteData6.map","// Same graphite assets, line-wrapped for editor stability\nexport const 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const __FramerMetadata__ = {\"exports\":{\"ART_7\":{\"type\":\"variable\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"__FramerMetadata__\":{\"type\":\"variable\"}}}\n//# sourceMappingURL=./GraphiteData7.map","// Same graphite assets, line-wrapped for editor stability\nexport const 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const __FramerMetadata__ = {\"exports\":{\"ART_8\":{\"type\":\"variable\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"__FramerMetadata__\":{\"type\":\"variable\"}}}\n//# sourceMappingURL=./GraphiteData8.map","import{ART_1}from\"https://framerusercontent.com/modules/nISbbxyevwrTGj3DSSns/CHSrYjokGRzFrqsgYfCu/GraphiteData1.js\";import{ART_2}from\"https://framerusercontent.com/modules/VbapFshZnFwQJTKYENvO/FsxHRsD5EUsy8Jvk9X56/GraphiteData2.js\";import{ART_3}from\"https://framerusercontent.com/modules/8cve2Q8z9JVeXqTFxwkm/i6o26mA9DxyAdvvlLySo/GraphiteData3.js\";import{ART_4}from\"https://framerusercontent.com/modules/3rTipYCkGmyawgZbqK11/u3PnOMIquuvkXEmNlMUw/GraphiteData4.js\";import{ART_5}from\"https://framerusercontent.com/modules/rV7GddclBSUFcb0YR1CU/9eygqzYJzLb9QM9khd06/GraphiteData5.js\";import{ART_6}from\"https://framerusercontent.com/modules/s6PsBCt93VPEZiSSKdvk/BALreJy2unkxTxhpgI5h/GraphiteData6.js\";import{ART_7}from\"https://framerusercontent.com/modules/ZTAR4PqIfP2pQg04Vsad/ajd2PTm5DfpJP0KukQXn/GraphiteData7.js\";import{ART_8}from\"https://framerusercontent.com/modules/OtmQ7SpOVeKqP2W0E9Oy/UX8tu4fybRbXFat79vby/GraphiteData8.js\";export const GRAPHITE_ART={...ART_1,...ART_2,...ART_3,...ART_4,...ART_5,...ART_6,...ART_7,...ART_8};\nexport const __FramerMetadata__ = {\"exports\":{\"GRAPHITE_ART\":{\"type\":\"variable\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"__FramerMetadata__\":{\"type\":\"variable\"}}}\n//# sourceMappingURL=./GraphiteAssets.map","import{jsx as _jsx,jsxs as _jsxs,Fragment as _Fragment}from\"react/jsx-runtime\";import*as React from\"react\";import{GRAPHITE_ART}from\"https://framerusercontent.com/modules/H9pR3MgmIQ72pWbhRzKZ/dKsQnMEz8yxQLgBQ021C/GraphiteAssets.js\";export{GRAPHITE_ART}from\"https://framerusercontent.com/modules/H9pR3MgmIQ72pWbhRzKZ/dKsQnMEz8yxQLgBQ021C/GraphiteAssets.js\";const ART_FAMILIES={documents:\"knowledge\",chunks:\"knowledge\",search:\"research\",citation:\"evidence\",workspace:\"agents\",models:\"models\",evaluation:\"models\",comparison:\"models\",routing:\"deployment\",task:\"agents\",tools:\"agents\",merge:\"agents\",action:\"approval\",premises:\"deployment\",boundary:\"policy\",hybrid:\"deployment\",audit:\"evidence\",portfolio:\"investment\",briefing:\"investment\",\"investment-review\":\"investment\",research:\"research\",\"research-connect\":\"research\",memo:\"research\",\"source-original\":\"knowledge\",policy:\"policy\",diff:\"validation\",exceptions:\"compliance\",credit:\"credit\",criteria:\"credit\",\"credit-memo\":\"credit\",request:\"operations\",operations:\"operations\",\"operations-review\":\"approval\",gate:\"policy\",hold:\"approval\",execution:\"agents\",evidence:\"evidence\",blocked:\"policy\",\"denied-record\":\"evidence\",ola:\"knowledge\",rynta:\"approval\",cases:\"investment\"};export function graphiteKind(kind){return ART_FAMILIES[kind]||kind;}export function GraphiteDrawing({kind=\"knowledge\",caption=\"\",label=\"\",reduced=false,className=\"\",onAdvance,study=false}){const state=useDrawing();const family=graphiteKind(kind);const source=GRAPHITE_ART[family]||GRAPHITE_ART.knowledge;const Tag=onAdvance?\"button\":\"div\";return /*#__PURE__*/_jsxs(Tag,{ref:state.ref,type:onAdvance?\"button\":undefined,onClick:onAdvance,className:`graphite-drawing ${state.active&&!state.reduced&&!reduced?\"drawing-active\":\"\"} ${className}`,\"aria-label\":onAdvance?`Next stage. ${label||caption}`:undefined,\"data-art-kind\":family,\"data-art-state\":kind,children:[study?/*#__PURE__*/_jsx(GraphiteStudy,{kind:kind},kind):/*#__PURE__*/_jsxs(\"div\",{className:`graphite-drawing-media motion-${family}`,children:[/*#__PURE__*/_jsx(\"img\",{src:source,alt:label||caption||`${family} workflow, illustrated in graphite`,loading:\"lazy\",decoding:\"async\"}),/*#__PURE__*/_jsx(\"div\",{className:\"graphite-drawing-print\",\"aria-hidden\":\"true\",children:/*#__PURE__*/_jsx(\"img\",{src:source,alt:\"\",loading:\"lazy\",decoding:\"async\"})})]},kind),caption&&/*#__PURE__*/_jsx(\"span\",{className:\"graphite-drawing-caption\",children:caption})]});}// Each stage is an independent pencil study; no shared photograph is swapped\n// beneath different labels. Text remains in the adjacent HTML narrative.\nfunction StudyPaper({x=0,y=0,w=150,h=180,angle=0,mark=0}){return /*#__PURE__*/_jsxs(\"g\",{transform:`translate(${x} ${y}) rotate(${angle} ${w/2} ${h/2})`,children:[/*#__PURE__*/_jsx(\"path\",{d:`M0 0H${w-22}L${w} 22V${h}H0Z`,fill:\"#f6f4ed\"}),/*#__PURE__*/_jsx(\"path\",{d:`M${w-22} 0V22H${w}`,className:\"study-fine\"}),[0,1,2,3,4].map(i=>/*#__PURE__*/_jsx(\"path\",{d:`M18 ${42+i*(h-65)/5}h${w-36-i%2*20}`,className:i===mark?\"study-accent\":\"study-fine\"},i)),/*#__PURE__*/_jsx(\"path\",{d:`M5 ${h+5}H${w+5}V30`,className:\"study-fine\"})]});}function StudyNode({x,y,r=36,type=0}){return /*#__PURE__*/_jsxs(\"g\",{transform:`translate(${x} ${y})`,children:[/*#__PURE__*/_jsx(\"circle\",{r:r,fill:\"#f6f4ed\"}),type===0?/*#__PURE__*/_jsxs(_Fragment,{children:[/*#__PURE__*/_jsx(\"circle\",{r:r*.35}),/*#__PURE__*/_jsx(\"path\",{d:`M${r*.25} ${r*.25}l${r*.3} 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IntersectionObserver(([entry])=>{if(entry.isIntersecting)setActive(true);},{threshold:.12});if(ref.current)observer.observe(ref.current);return()=>{observer.disconnect();query.removeEventListener(\"change\",update);};},[]);return{ref,active,reduced};}export function GraphiteHome({olaHref,ryntaHref,Link=\"a\"}){const{ref,active,reduced}=useDrawing();return /*#__PURE__*/_jsxs(\"section\",{className:\"graphite-home\",\"aria-labelledby\":\"graphite-home-title\",children:[/*#__PURE__*/_jsx(\"style\",{children:GRAPHITE_CSS}),/*#__PURE__*/_jsxs(\"div\",{ref:ref,className:`graphite-picture ${active&&!reduced?\"drawing-active\":\"\"}`,children:[/*#__PURE__*/_jsxs(\"div\",{className:\"graphite-paper\",\"aria-hidden\":\"true\",children:[/*#__PURE__*/_jsx(\"img\",{src:GRAPHITE_ART.home,alt:\"\",width:\"1536\",height:\"1024\",decoding:\"async\",fetchPriority:\"high\"}),/*#__PURE__*/_jsx(\"div\",{className:\"graphite-ink-pass\",children:/*#__PURE__*/_jsx(\"img\",{src:GRAPHITE_ART.home,alt:\"\",width:\"1536\",height:\"1024\",decoding:\"async\"})})]}),/*#__PURE__*/_jsxs(\"div\",{className:\"graphite-title\",children:[/*#__PURE__*/_jsx(\"h1\",{id:\"graphite-home-title\",children:\"AI for real work\"}),/*#__PURE__*/_jsxs(\"p\",{children:[\"Knowledge into action\",/*#__PURE__*/_jsx(\"span\",{className:\"graphite-copy-separator\",children:\" \\xb7 \"}),/*#__PURE__*/_jsx(\"br\",{className:\"graphite-mobile-break\"}),\"People in control\"]})]})]}),/*#__PURE__*/_jsxs(\"div\",{className:\"graphite-brand-strip\",children:[/*#__PURE__*/_jsxs(Link,{href:olaHref,className:\"graphite-brand\",children:[/*#__PURE__*/_jsx(\"strong\",{children:\"OLA\"}),/*#__PURE__*/_jsxs(\"p\",{children:[\"Turn scattered knowledge\",/*#__PURE__*/_jsx(\"br\",{}),\"into clarity and action\"]}),/*#__PURE__*/_jsx(\"span\",{\"aria-hidden\":\"true\",children:\"↗\"})]}),/*#__PURE__*/_jsxs(Link,{href:ryntaHref,className:\"graphite-brand\",children:[/*#__PURE__*/_jsx(\"strong\",{children:\"RYNTA\"}),/*#__PURE__*/_jsxs(\"p\",{children:[\"Keep people in control\",/*#__PURE__*/_jsx(\"br\",{}),\"where it matters\"]}),/*#__PURE__*/_jsx(\"span\",{\"aria-hidden\":\"true\",children:\"↗\"})]})]})]});}export const GRAPHITE_CSS=`\n.onev2 .graphite-home,.graphite-home{--graphite-ink:#202527;--graphite-paper:#f6f4ed;--graphite-blue:#385c6f;color:var(--graphite-ink);background:var(--graphite-paper);isolation:isolate}\n.graphite-home *{box-sizing:border-box}\n.graphite-picture{position:relative;width:min(100%,calc((100svh - var(--header-h,86px))*1.5));aspect-ratio:1.5;overflow:hidden;background:var(--graphite-paper);margin-inline:auto}\n.graphite-paper{position:absolute;inset:0}\n.graphite-paper>img,.graphite-ink-pass>img{position:absolute;width:100%;height:100%;inset:0;object-fit:contain;display:block}\n.graphite-paper>img{opacity:1}.drawing-active .graphite-paper>img{animation:graphiteUnderprint 2.4s ease both}\n.graphite-title{position:relative;z-index:2;padding:5.7% 4% 0;width:83%;pointer-events:none}\n.onev2 .graphite-title h1,.graphite-title h1{font-family:Georgia,'Times New Roman',serif;font-weight:500;font-size:clamp(32px,min(6.4vw,10svh),104px);line-height:1.03;letter-spacing:-.065em;color:var(--graphite-ink);margin:0;white-space:nowrap}\n.onev2 .graphite-title p,.graphite-title p{font-family:Georgia,'Times New Roman',serif;font-size:clamp(16px,min(2.1vw,3.3svh),34px);line-height:1.22;letter-spacing:-.035em;color:var(--graphite-ink);margin:clamp(12px,1.7svh,20px) 0 0}\n.graphite-mobile-break{display:none}\n.graphite-ink-pass{position:absolute;inset:0;clip-path:inset(0 0 0 0)}\n.drawing-active .graphite-ink-pass{animation:graphitePrint 2.4s cubic-bezier(.2,.6,.3,1) both}\n.graphite-marks{position:absolute;inset:0;width:100%;height:100%;pointer-events:none;z-index:1}\n.graphite-mark{stroke:var(--graphite-blue);stroke-width:2;fill:none;opacity:.35;stroke-linecap:round;stroke-dasharray:700;stroke-dashoffset:0}\n.drawing-active .graphite-mark{animation:graphitePencil 1.9s cubic-bezier(.3,.6,.2,1) both}\n.drawing-active .graphite-mark-two{animation-delay:.7s}.drawing-active .graphite-mark-three{animation-delay:1.2s}\n.graphite-brand-strip{display:grid;grid-template-columns:1fr 1fr;gap:0;padding:30px 6% 38px;border-top:1px solid #d4d6cf}\n.graphite-brand{display:flex;align-items:center;gap:28px;text-decoration:none;color:inherit;padding:12px 6%;min-height:95px}\n.graphite-brand+.graphite-brand{border-left:1px solid #c7ccc7}\n.graphite-brand strong{font:400 clamp(30px,2.6vw,48px)/1.1 Georgia,'Times New Roman',serif;color:var(--graphite-blue);letter-spacing:-.025em}\n.onev2 .graphite-brand p,.graphite-brand p{font:400 clamp(18px,1.5vw,25px)/1.35 Georgia,'Times New Roman',serif;color:var(--graphite-ink);margin:0;border-left:1px solid #c7ccc7;padding-left:24px}\n.graphite-brand>span{font:25px/1 Arial,sans-serif;margin-left:auto;transition:transform .25s ease}\n.graphite-brand:hover>span,.graphite-brand:focus-visible>span{transform:translate(3px,-3px)}\n.graphite-brand:focus-visible{outline:3px solid #385c6f;outline-offset:4px}\n.graphite-drawing{display:block;position:relative;width:100%;padding:0;border:0;background:#f6f4ed;text-align:left;color:#334b56;min-width:0;overflow:hidden}\n.graphite-drawing-media{position:relative;aspect-ratio:1.5;overflow:hidden}\n.graphite-drawing-media>img,.graphite-drawing-print img{display:block;position:absolute;inset:0;width:100%;height:100%;object-fit:contain}\n.graphite-drawing-print{position:absolute;inset:0;pointer-events:none}\n.drawing-active .graphite-drawing-media>img{animation:graphiteUnderprint 1.65s ease both}\n.drawing-active .graphite-drawing-print{animation:graphitePrint 1.65s cubic-bezier(.18,.6,.3,1) both}\n.graphite-drawing-caption{display:block;padding:18px 3%;font:18px/1.45 Arial,sans-serif;color:#405662;border-top:1px solid #cdd3cd}\n.graphite-drawing:focus-visible{outline:3px solid #385c6f;outline-offset:-3px}\n.graphite-study{width:100%;aspect-ratio:5/3;position:relative;overflow:hidden;background:#f6f4ed}\n.graphite-study>svg{width:100%;height:100%;display:block}\n.study-ink .study-fine{stroke:#899b9e;stroke-width:1.2}\n.study-ink .study-accent{stroke:#577d8d;stroke-width:4}\n.study-flow{stroke:#6b8b98;stroke-width:2.5;stroke-dasharray:1;stroke-dashoffset:0}\n.drawing-active .study-flow{animation:studyTrace 1.7s cubic-bezier(.22,.68,.22,1) both}\n.drawing-active .study-piece{animation:studyGather 1s cubic-bezier(.22,.68,.22,1) both;animation-delay:calc(var(--i,0)*.14s)}\n.drawing-active .study-gather .study-ink>g{animation:studyGather 1.4s cubic-bezier(.2,.7,.2,1) both}\n.drawing-active .study-gather .study-ink>g:nth-child(2n){animation-name:studyGatherRight;animation-delay:.2s}\n.drawing-active .study-split .study-piece{animation-name:studyUnfold}\n.drawing-active .study-inspect .study-inspector{animation:studyInspect 2.4s cubic-bezier(.3,.65,.2,1) both}\n.drawing-active .study-scan .study-ink>g{animation:studyScan 1.4s ease both}\n.drawing-active .study-scan .study-ink>g:nth-child(2n){animation-delay:.24s}\n.drawing-active .study-compare .study-ink>g{animation:studyCompare 1.4s cubic-bezier(.2,.7,.2,1) both}\n.drawing-active .study-compare .study-ink>g:nth-child(2n){animation-delay:.2s}\n.drawing-active .study-route .study-ink>g{animation:studyRoute 1.7s cubic-bezier(.2,.7,.2,1) both}\n.drawing-active .study-build .study-ink{animation:studyBuild 1.8s cubic-bezier(.2,.7,.2,1) both}\n.drawing-active .study-annotate .study-flow{animation-duration:2.5s}\n.drawing-active .study-write .study-accent{stroke-dasharray:500;animation:studyWrite 1.8s ease both}\n.drawing-active .study-hold .study-accent{animation:studyDecision 1.4s ease both}\n.drawing-active .study-orbit .study-ink{transform-origin:450px 260px;animation:studyTurn 2s cubic-bezier(.2,.7,.2,1) both}\n.drawing-active .motion-models .graphite-drawing-print{animation-name:graphiteCompare}\n.drawing-active .motion-agents .graphite-drawing-print{animation-name:graphiteBranches;animation-duration:2.5s}\n.drawing-active .motion-deployment .graphite-drawing-print{animation-name:graphiteBridge;animation-duration:2.6s}\n.drawing-active .motion-policy .graphite-drawing-print{animation-name:graphiteBoundary}\n.drawing-active .motion-validation .graphite-drawing-print{animation-name:graphiteCompare;animation-duration:2.2s}\n.drawing-active .motion-approval .graphite-drawing-print{animation-name:graphiteDecision;animation-duration:2.2s}\n.drawing-active .motion-investment .graphite-drawing-print{animation-name:graphiteRadial;animation-duration:2.3s}\n.drawing-active .motion-credit .graphite-drawing-print{animation-name:graphiteLayers;animation-duration:2.4s}\n.drawing-active .motion-operations .graphite-drawing-print{animation-timing-function:steps(5,end);animation-duration:2.3s}\n.drawing-active .motion-compliance .graphite-drawing-print{animation-name:graphiteCompare;animation-duration:2.5s}\n.graphite-assembly{width:100%;height:100%;position:relative;overflow:hidden}\n.graphite-assembly svg{display:block;width:100%;height:100%;overflow:visible}\n.assembly-running .assembly-piece{animation:assemblyFit 3.1s cubic-bezier(.2,.72,.2,1) both;animation-delay:calc(var(--i)*.28s)}\n@keyframes assemblyFit{0%,12%{transform:translate(var(--dx),var(--dy));opacity:.75}70%,100%{transform:translate(0,0);opacity:1}}\n@keyframes studyTrace{from{stroke-dashoffset:1}to{stroke-dashoffset:0}}\n@keyframes studyGather{from{translate:-38px 16px;opacity:.35}to{translate:0 0;opacity:1}}\n@keyframes studyGatherRight{from{translate:38px -16px;opacity:.35}to{translate:0 0;opacity:1}}\n@keyframes studyUnfold{from{translate:-60px 0;opacity:.3}to{translate:0 0;opacity:1}}\n@keyframes studyInspect{0%{translate:-110px 60px}100%{translate:0 0}}\n@keyframes studyScan{from{clip-path:inset(0 0 100% 0)}to{clip-path:inset(0)}}\n@keyframes studyCompare{from{translate:0 25px;opacity:.35}to{translate:0 0;opacity:1}}\n@keyframes studyRoute{from{translate:-45px 0;opacity:.35}to{translate:0 0;opacity:1}}\n@keyframes studyBuild{from{clip-path:inset(100% 0 0 0)}to{clip-path:inset(0)}}\n@keyframes studyWrite{from{stroke-dashoffset:500}to{stroke-dashoffset:0}}\n@keyframes studyDecision{0%,55%{stroke-opacity:.3}100%{stroke-opacity:1}}\n@keyframes studyTurn{from{rotate:-12deg;opacity:.4}to{rotate:0deg;opacity:1}}\n@keyframes graphiteCompare{0%{clip-path:inset(0 67% 0 0)}45%{clip-path:inset(0 33% 0 0)}100%{clip-path:inset(0)}}\n@keyframes graphiteBranches{0%{clip-path:polygon(0 20%,25% 20%,25% 80%,0 80%)}50%{clip-path:polygon(0 0,70% 0,70% 100%,0 100%)}100%{clip-path:polygon(0 0,100% 0,100% 100%,0 100%)}}\n@keyframes graphiteBridge{0%{clip-path:inset(0 53% 0 0)}55%{clip-path:inset(0 38% 0 0)}100%{clip-path:inset(0)}}\n@keyframes graphiteBoundary{from{clip-path:inset(45% 45% 45% 45%)}to{clip-path:inset(0)}}\n@keyframes graphiteDecision{from{clip-path:inset(48% 0 0 0)}to{clip-path:inset(0)}}\n@keyframes graphiteRadial{from{clip-path:circle(3% at 50% 50%)}to{clip-path:circle(75% at 50% 50%)}}\n@keyframes graphiteLayers{0%{clip-path:inset(76% 0 0 0)}35%{clip-path:inset(50% 0 0 0)}65%{clip-path:inset(25% 0 0 0)}100%{clip-path:inset(0)}}\n@keyframes graphiteUnderprint{0%,72%{opacity:.22}100%{opacity:1}}\n@keyframes graphitePrint{from{clip-path:inset(0 100% 0 0)}to{clip-path:inset(0 0 0 0)}}\n@keyframes graphitePencil{from{stroke-dashoffset:700;opacity:0}to{stroke-dashoffset:0;opacity:.35}}\n/* Stack text above the uncropped artwork on phones and portrait tablets. */\n@media(max-width:800px) and (min-height:601px){\n .graphite-picture{width:100%;aspect-ratio:auto;min-height:0;display:flex;flex-direction:column}\n .graphite-title{width:100%;padding:28px 6% 0;order:0}\n .onev2 .graphite-title h1,.graphite-title h1{font-size:clamp(36px,10vw,56px);white-space:normal;letter-spacing:-.05em}\n .onev2 .graphite-title p,.graphite-title p{font-size:20px;line-height:1.35;letter-spacing:-.025em;margin-top:14px}\n .graphite-mobile-break{display:block}.graphite-copy-separator{display:none}\n .graphite-paper{position:relative;order:1;margin-top:16px;width:100%;aspect-ratio:1.5;height:auto;overflow:hidden}\n .graphite-paper>img,.graphite-ink-pass>img{object-fit:contain;object-position:center;width:100%;height:100%}\n .graphite-marks{display:none}\n}\n@media(max-width:800px){\n .graphite-brand-strip{padding:8px 6% 20px;grid-template-columns:1fr}\n .graphite-brand{padding:24px 0;gap:20px;min-height:0}\n .graphite-brand+.graphite-brand{border-left:0;border-top:1px solid #d0d4cc}\n .graphite-brand strong{min-width:85px;font-size:32px}\n .onev2 .graphite-brand p,.graphite-brand p{font-size:18px;padding-left:18px}\n .graphite-brand>span{font-size:22px}\n}\n@media(max-width:430px){\n .graphite-brand-strip{padding-bottom:8px}.graphite-brand{gap:12px}\n .graphite-brand strong{min-width:78px;font-size:29px}\n .onev2 .graphite-brand p,.graphite-brand p{font-size:18px;padding-left:12px}\n .graphite-brand>span{font-size:20px}\n}\n@media(prefers-reduced-motion:reduce){.graphite-home *,.graphite-drawing *{animation:none!important;transition:none!important}.graphite-ink-pass{clip-path:none!important}}\n/* Fill the viewport while preserving the artwork's natural proportions. */\n.graphite-picture{width:100%;height:calc(100svh - var(--header-h,86px));min-height:460px;aspect-ratio:auto;margin:0}\n.graphite-paper>img,.graphite-ink-pass>img{object-fit:cover;object-position:50% 64%}\n.graphite-title{padding:clamp(28px,5svh,56px) 5% 0;width:83%}\n.graphite-picture::after{content:\"\";position:absolute;inset:0;pointer-events:none;background:radial-gradient(ellipse 68% 48% at 16% 0%,rgba(246,244,237,.97),rgba(246,244,237,.84) 46%,transparent 100%);z-index:1}\n@media(max-width:800px) and (min-height:601px){\n .graphite-picture{display:grid;grid-template-rows:auto minmax(0,1fr);min-height:560px}\n .graphite-picture::after{display:none}\n .graphite-title{padding:28px 6% 0;width:100%}\n .graphite-paper{height:100%;min-height:0;aspect-ratio:auto;margin:0;inset:auto}\n .graphite-paper>img,.graphite-ink-pass>img{object-fit:cover;object-position:51% 65%}\n}\n@media(max-height:600px) and (min-width:700px){.graphite-picture{min-height:280px}.graphite-title{padding-top:22px}}\n/* Place the headline within the drawing's open paper area. */\n.graphite-title{position:absolute;top:11%;left:8%;width:64%;padding:0;text-align:center}\n.graphite-picture::after{background:radial-gradient(ellipse 48% 36% at 40% 20%,rgba(246,244,237,.88),rgba(246,244,237,.64) 32%,transparent 78%)}\n@media(max-width:800px) and (min-height:601px){\n .graphite-picture{display:block}\n .graphite-paper{position:absolute;inset:0;width:100%;height:100%;margin:0}\n .graphite-title{top:9%;left:7%;width:86%;padding:0;text-align:center}\n .graphite-picture::after{display:block;background:radial-gradient(ellipse 90% 30% at 50% 17%,rgba(246,244,237,.86),rgba(246,244,237,.58) 44%,transparent 100%)}\n}\n@media(max-height:600px) and (min-width:700px){.graphite-title{top:9%;padding:0}}\n/* Keep the approved type scale and vertical placement; align the copy left. */\n.graphite-title{text-align:left}\n.graphite-picture::after{background:radial-gradient(ellipse 48% 34% at 24% 20%,rgba(246,244,237,.97),rgba(246,244,237,.90) 48%,rgba(246,244,237,.55) 72%,transparent 100%)}\n@media(max-width:800px) and (min-height:601px){\n .graphite-title{text-align:left}\n .graphite-picture::after{background:radial-gradient(ellipse 90% 30% at 33% 17%,rgba(246,244,237,.94),rgba(246,244,237,.78) 48%,transparent 100%)}\n}\n`;\nexport const __FramerMetadata__ = {\"exports\":{\"graphiteKind\":{\"type\":\"function\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"GRAPHITE_ART\":{\"type\":\"variable\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"GraphiteAssembly\":{\"type\":\"reactComponent\",\"name\":\"GraphiteAssembly\",\"slots\":[],\"annotations\":{\"framerContractVersion\":\"1\"}},\"GraphiteDrawing\":{\"type\":\"reactComponent\",\"name\":\"GraphiteDrawing\",\"slots\":[],\"annotations\":{\"framerContractVersion\":\"1\"}},\"GRAPHITE_CSS\":{\"type\":\"variable\",\"annotations\":{\"framerContractVersion\":\"1\"}},\"GraphiteHome\":{\"type\":\"reactComponent\",\"name\":\"GraphiteHome\",\"slots\":[],\"annotations\":{\"framerContractVersion\":\"1\"}},\"__FramerMetadata__\":{\"type\":\"variable\"}}}\n//# sourceMappingURL=./GraphiteEditorial.map","import{jsx as _jsx,jsxs as _jsxs,Fragment as _Fragment}from\"react/jsx-runtime\";import{JOURNAL_1}from\"https://framerusercontent.com/modules/HnEuVOb6TBeHe7qM3LGV/dU7DYiGwMAo9E4L44kIZ/GraphiteResearch1.js\";import{JOURNAL_2}from\"https://framerusercontent.com/modules/iVDTN5UCIklQIWHm8S24/Z6dWR6zXXoUsTAKHELiV/GraphiteResearch2.js\";import{GraphiteHome,GraphiteDrawing,GraphiteAssembly,GRAPHITE_ART,GRAPHITE_CSS}from\"https://framerusercontent.com/modules/KKz4cj2B0dQMU8ikhf87/tkAY9hw4xv2vB4dp8IDi/GraphiteEditorial.js\";import*as React from\"react\";import{motion}from\"framer-motion\";import{addPropertyControls,ControlType,Link as FramerLink,useIsStaticRenderer,useLocaleInfo}from\"framer\";// OneLineAI V2 — independently authored, unpublished review experience.\n// Content basis: OLA AX service introduction, 16 September 2026.\nconst PAGES=[\"Home\",\"OLA\",\"Knowledge\",\"Models\",\"Agents\",\"Deployment\",\"Use Cases\",\"Investment\",\"Research\",\"Compliance\",\"Credit\",\"Internal Operations\",\"Company\",\"Contact\",\"Team\",\"RYNTA\",\"Perspectives\",\"Stories\",\"Journal 01\",\"Journal 02\",\"Journal 03\",\"Journal 04\",\"Journal 05\",\"Journal 06\",\"Journal 07\",\"Journal 08\",\"Journal 09\",\"Journal 10\",\"Journal 11\",\"Journal 12\",\"Journal 13\",\"Journal 14\",\"Journal 15\",\"Journal 16\",\"Journal 17\",\"Journal 18\",\"Journal 19\",\"Journal 20\",\"Journal 21\",\"Journal 22\",\"Journal 23\",\"Journal 24\",\"Journal 25\",\"Journal 26\",\"Journal 27\",\"Journal 28\",\"Journal 29\",\"Journal 30\",\"Journal 31\",\"Journal 32\",\"Journal 33\"];const SLUGS=[\"\",\"/ola\",\"/ola/knowledge\",\"/ola/models\",\"/ola/agents\",\"/ola/deployment\",\"/use-cases\",\"/use-cases/investment\",\"/use-cases/research\",\"/use-cases/compliance\",\"/use-cases/credit\",\"/use-cases/internal-operations\",\"/company\",\"/contact\",\"/company/team\",\"/rynta\",\"/research\",\"/stories\",\"/research/ko-r1-3-data-mix\",\"/research/ko-r1-3-data-mix-ko\",\"/research/ko-r1-3-development\",\"/research/ko-r1-3-development-ko\",\"/research/spot-verification\",\"/research/spot-verification-ko\",\"/research/multilingual-test-time-scaling\",\"/research/multilingual-test-time-scaling-ko\",\"/research/korean-multiplication\",\"/research/won-financial-model\",\"/research/ko-r1-1-5b\",\"/research/ko-r1-1-5b-ko\",\"/research/olaf2-test-time-scaling\",\"/research/olaf2-test-time-scaling-ko\",\"/research/korean-reasoning-benchmarks\",\"/research/korean-reasoning-benchmarks-ko\",\"/research/olaf-v2\",\"/research/qwq-longcot\",\"/research/hrm8k\",\"/research/krx-competition\",\"/research/synthetic-data-self-instruct\",\"/research/synthetic-data-wizardlm\",\"/research/synthetic-data-cosmopedia\",\"/research/financial-reasoning\",\"/research/hae-rae-bench\",\"/research/krx-bench\",\"/research/kmmlu\",\"/research/multi-task-inference\",\"/research/k2-eval\",\"/research/model-evaluation\",\"/stories/financial-conversations\",\"/stories/report-workflows\",\"/stories/your-data-your-workspace\"];const 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