OBJECTIVE To construct a large language model for TCM question-answering.

METHODS TCM corpora were built by collecting TCM classics such as Treatise on Cold Damage, TCM textbooks, prescriptions from famous TCM doctors, and other manually annotated TCM datasets. A TCM knowledge vector library was constructed. The RAG technology was fused with the P-Tuning v2 fine-tuning method and the large language model (ChatGLM2-6B) to build the TCM question-answering large language model.

RESULTS Recision, Recall, and F1 score were used as evaluation metrics for knowledge question-answering tasks. The model achieved over 90% accuracy in simple TCM question-answering, with the highest accuracy in component-type questions, reaching an F1 score of 0.928. The accuracy of medium to high difficulty questions ranged from 75.8% to 87.7%, with F1 scores all exceeding 0.766. Expert ratings based on diversity and accuracy were used as evaluation metrics for TCM question generation tasks, and the model in this paper scored 9.5 points higher than the baseline model.

CONCLUSION The model in this paper demonstrates good semantic understanding and high reliability, effectively alleviating model hallucinations and helping patients clarify their question intentions. It is of great significance for advancing research on TCM knowledge and providing personalized interactive answers. It also provides an innovative approach to promoting the inheritance and popularization of TCM experience and the intelligent construction of TCM diagnosis and treatment.