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HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs

About

The breakthrough of OpenAI o1 highlights the potential of enhancing reasoning to improve LLM. Yet, most research in reasoning has focused on mathematical tasks, leaving domains like medicine underexplored. The medical domain, though distinct from mathematics, also demands robust reasoning to provide reliable answers, given the high standards of healthcare. However, verifying medical reasoning is challenging, unlike those in mathematics. To address this, we propose verifiable medical problems with a medical verifier to check the correctness of model outputs. This verifiable nature enables advancements in medical reasoning through a two-stage approach: (1) using the verifier to guide the search for a complex reasoning trajectory for fine-tuning LLMs, (2) applying reinforcement learning (RL) with verifier-based rewards to enhance complex reasoning further. Finally, we introduce HuatuoGPT-o1, a medical LLM capable of complex reasoning, which outperforms general and medical-specific baselines using only 40K verifiable problems. Experiments show complex reasoning improves medical problem-solving and benefits more from RL. We hope our approach inspires advancements in reasoning across medical and other specialized domains.

Junying Chen, Zhenyang Cai, Ke Ji, Xidong Wang, Wanlong Liu, Rongsheng Wang, Jianye Hou, Benyou Wang• 2024

Related benchmarks

TaskDatasetResultRank
Medical Question AnsweringMedMCQA
Accuracy76.76
253
Medical Question AnsweringMedMCQA (test)
Accuracy73.61
134
Multiple-choice Question AnsweringMMLU-Pro
MMLU-Pro Overall Accuracy57.58
116
Medical Question AnsweringMedQA
Accuracy88.85
109
Question AnsweringMedQA-USMLE (test)
Accuracy83.27
101
Natural Language InferenceMedNLI (test)
Accuracy62.34
89
Question AnsweringPubMedQA (test)
Accuracy80.6
81
Question AnsweringMedQA
Accuracy60.8
70
Medical Question AnsweringMedExpQA
Accuracy (English)80.32
61
Multilingual Multiple-Choice Question AnsweringHeadQA 1.0 (test)
Chinese Acc84.34
56
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