Summary of Enhancing the Traditional Chinese Medicine Capabilities Of Large Language Model Through Reinforcement Learning From Ai Feedback, by Song Yu et al.
Enhancing the Traditional Chinese Medicine Capabilities of Large Language Model through Reinforcement Learning from AI Feedback
by Song Yu, Xiaofei Xu, Fangfei Xu, Li Li
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 The paper proposes a framework to improve large language models’ performance in Traditional Chinese Medicine (TCM) tasks using only a small amount of data. The approach involves supervised fine-tuning of the model with medical case data, allowing it to perform initial TCM tasks. Additionally, reinforcement learning from AI feedback (RLAIF) is applied to align the model’s output with user preferences. Experimental results demonstrate that the model achieves significant performance improvements on representative TCM tasks with a small amount of data. This work addresses the limitations of large language models in specialized domains like TCM and has implications for natural language processing applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers understand and respond to questions about Traditional Chinese Medicine (TCM). Right now, these computers don’t do well with TCM because they lack expertise. To improve their performance, the researchers created a new way to train computers using only a little bit of data. They first taught the computer to perform simple tasks related to TCM. Then, they used special feedback from artificial intelligence (AI) to help the computer make better decisions. The results show that this approach can significantly improve how well the computer performs on important TCM tasks. |
Keywords
» Artificial intelligence » Fine tuning » Natural language processing » Reinforcement learning » Supervised
