Summary of Huatuogpt-o1, Towards Medical Complex Reasoning with Llms, by Junying Chen et al.
HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs
by Junying Chen, Zhenyang Cai, Ke Ji, Xidong Wang, Wanlong Liu, Rongsheng Wang, Jianye Hou, Benyou Wang
First submitted to arxiv on: 25 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This abstract discusses the potential of enhancing reasoning to improve Large Language Models (LLMs) in the medical domain, which has been underexplored compared to mathematical tasks. The authors propose verifiable medical problems with a medical verifier to check the correctness of model outputs, enabling advancements in medical reasoning through two stages: fine-tuning LLMs using a complex reasoning trajectory and applying reinforcement learning with verifier-based rewards. They introduce HuatuoGPT-o1, a medical LLM capable of complex reasoning that outperforms general and medical-specific baselines using only 40K verifiable problems. The authors also demonstrate that complex reasoning improves medical problem-solving and benefits more from reinforcement learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using artificial intelligence to help doctors make better decisions. It’s hard to verify if an AI system is making good decisions, especially in the medical field where mistakes can be very serious. To solve this problem, the authors came up with a new way to test AI systems by giving them a set of medical problems to solve. They then used this testing method to improve their AI system so it could make better decisions on its own. The results show that this improved AI system is much better at solving complex medical problems than other similar AI systems. |
Keywords
» Artificial intelligence » Fine tuning » Reinforcement learning
