Research on a Traditional Chinese Medicine Knowledge Q&A Model Integrating Supervised Fine-Tuning and Retrieval-Augmented Generation
- VernacularTitle:融合监督微调和检索增强的中医知识问答模型研究
- Author:
Xinyu WANG
1
;
Tao YANG
;
Song WANG
;
Yichu XU
;
Kongfa HU
Author Information
- Publication Type:Journal Article
- Keywords: Supervised fine-tuning; Retrieval-augmented generation; Large language model; TCM knowledge question answering
- From: World Science and Technology-Modernization of Traditional Chinese Medicine 2025;27(7):1898-1905
- CountryChina
- Language:Chinese
- Abstract: Objective To construct a traditional Chinese medicine(TCM)knowledge question-answering model with strong reasoning capabilities and reliable results,TCM Q&A datasets and TCM literature were fully utilized.Methods Large-scale TCM corpus and Q&A data were collected and organized,with ChatGLM3 serving as the base model.The PissA method was used for supervised fine-tuning,combined with retrieval-augmented generation(RAG)techniques,to build a TCM knowledge Q&A model that integrates supervised fine-tuning and retrieval-augmented generation.The model was compared with ChatGLM3,SFT,and RAG,with evaluations based on classic metrics such as BLEU,ROUGE1,and F-scores.Results The model in this paper achieved BLEU and ROUGE1 scores of 14.5830 and 34.6730,respectively.After incorporating retrieval-augmented generation,the model attained an F score of 0.6398 in the inference results on a TCM dataset,outperforming the ChatGLM3 baseline model's 0.2654.Conclusion The construction method of a large model in the TCM domain that integrates supervised fine-tuning and retrieval augmentation can effectively enhance the model's reasoning performance and reliability in TCM.
