融合思维链与知识图谱的中医问答模型OA北大核心

In response to the large scale of data in the field of Chinese medicine diagnosis,as well as the high subjectivity of doctors in diagnosis and the difficulty of data alignment,ChatTCM,a large language model for the Q&A domain of traditional Chinese medicine(TCM),is proposed.Taking advantage of the power of large language model(LLM)in dealing with natural language understanding and text generation,and fine-tuning the large language model,the LLM has expertise and competence in the field of TCM Q&A,thus preventing the model from generating hallucinations.Firstly,extract triplet information from TCM books to construct a TCM knowledge graph database,achieving data alignment and systematic integration of TCM knowledge,while providing background knowledge for large language models to generate answers.Secondly,integrate chain-of-thought(COT)reasoning with dynamic interactions from the knowledge graph database to generate an objective reasoning process,ensuring that the diagnostic recommendations are based on scientific evidence.Additionally,store the reasoning results from the chain-of-thought and knowledge graph as new knowledge,continuously expanding the local knowledge base.The ChatTCM model improves the BLEU-4 and ROUGE-L metrics on the MedChatZH dataset by 10.6 and 10.5 percentage points,respectively,and achieves 70%accuracy on the open-source dataset,which is a 10 percentage points improvement over the same type of MedChatZH model.