QingNangTCM:一种面向中医领域的参数高效微调大语言模型OA

Objective To develop QingNangTCM,a specialized large language model(LLM)tailored for expert-level traditional Chinese medicine(TCM)question-answering and clinical reasoning,addressing the scarcity of domain-specific corpora and specialized alignment. Methods We constructed QnTCM_Dataset,a corpus of 100 000 entries,by integrating data from ShenNong_TCM_Dataset and SymMap v2.0,and synthesizing additional samples via re-trieval-augmented generation(RAG)and persona-driven generation.The dataset compre-hensively covers diagnostic inquiries,prescriptions,and herbal knowledge.Utilizing P-Tun-ing v2,we fine-tuned the GLM-4-9B-Chat backbone to develop QingNangTCM.A multi-dimensional evaluation framework,assessing accuracy,coverage,consistency,safety,profes-sionalism,and fluency,was established using metrics such as bilingual evaluation under-study(BLEU),recall-oriented understudy for gisting evaluation(ROUGE),metric for evalua-tion of translation with explicit ordering(METEOR),and LLM-as-a-Judge with expert review.Qualitative analysis was conducted across four simulated clinical scenarios:symptom analy-sis,disease treatment,herb inquiry,and failure cases.Baseline models included GLM-4-9B-Chat,DeepSeek-V2,HuatuoGPT-II(7B),and GLM-4-9B-Chat(freeze-tuning). Results QingNangTCM achieved the highest scores in BLEU-1/2/3/4(0.425/0.298/0.137/0.064),ROUGE-1/2(0.368/0.157),and METEOR(0.218),demonstrating a balanced and supe-rior normalized performance profile of 0.900 across the dimensions of accuracy,coverage,and consistency.Although its ROUGE-L score(0.299)was lower than that of HuatuoGPT-II(7B)(0.351),it significantly outperformed domain-specific models in expert-validated win rates for professionalism(86％)and safety(73％).Qualitative analysis confirmed that the mod-el strictly adheres to the"symptom-syndrome-pathogenesis-treatment"reasoning chain,though occasional misclassifications and hallucinations persisted when dealing with rare medicinal materials and uncommon syndromes. Conclusion Combining domain-specific corpus construction with parameter-efficient prompt tuning enhances the reasoning behavior and domain adaptation of LLMs for TCM-related tasks.This work provides a technical framework for the digital organization and intel-ligent utilization of TCM knowledge,with potential value for supporting diagnostic reasoning and medical education.