TCMChat:传统中医学的生成性大语言模型
Yizheng Dai1, Xin Shao2, Jinlu Zhang1
1Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; State Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314103, China.
Pharmacological research
|December 1, 2024
概括
新的生成大语言模型 (LLM) TCMChat通过对广泛的TCM数据进行微调来增强传统中医 (TCM) 专业知识. 这种先进的AI模型在TCM知识任务中提供了卓越的性能.
科学领域:
- 人工智能的人工智能
- 计算语言学 计算语言学
- 传统中国医药 传统中国医药
背景情况:
- 大型语言模型 (LLM) 在医学中越来越多地使用,但它们在中国传统医学 (TCM) 中的应用有限.
- 现有的以TCM为重点的LLM已经显示出有限的能力.
研究的目的:
- 开发一个专业的生成法学士,TCMChat,在中国传统医学的增强专业知识.
- 提高LLMs在处理TCM特定知识和问答任务方面的表现.
主要方法:
- 编制了一个大规模的精选数据集,涵盖了六个TCM场景:知识库,多选题,阅读理解,实体提取,病例诊断和草药/配方建议.
- 在使用精心策划的TCM数据集的Baichuan2-7B-Chat基础模型上进行员工预培训 (PT) 和监督微调 (SFT).
- 使用文本挖掘和手动验证来编译数据集.
主要成果:
- 与对比数据集的现有模型相比,TCMChat表现优越.
- 案例研究证实了TCMChat在各种TCM相关任务中的有效性.
- 该模型为TCM现代化实现了高质量的知识表示.
结论:
- 在将LLMs应用于传统中医药方面,TCMChat代表了重大进步.
- 公开发布的代码,数据和模型促进了TCM现代化的进一步研究和开发.
- TCMChat提供了一个用户友好的对话网络工具,用于访问TCM知识.
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