@article{Kang2025, 
author = {Yanlan Kang and Yang Chang and Sunsi Wu and Xuening Wu and Yuqi Jiao and Jiyuan Fu and Qingshan Ma and Yide Fang and Yue Chen and Xue Zhao and Xukun Zhang and Jingyi Zhu and Xiyu Liu and Yan Wang and Haofen Wang and William Cheng-Chung Chu and Wenqiang Zhang},
title = {ZhongJingGPT: An Expert Knowledge-Guided Language Model for Traditional Chinese Medicine},
year = {2025},
journal = {Tsinghua Science and Technology},
keywords = {Evaluation, Traditional Chinese Medicine, Large Language Model, Medical Finite State Machine, Multi-scenario instructions},
url = {https://www.sciopen.com/article/10.26599/TST.2025.9010046},
doi = {10.26599/TST.2025.9010046},
abstract = {Traditional Chinese Medicine (TCM) presents unique challenges for large language models (LLMs) due to its complex diagnostic reasoning. We introduce ZhongJingGPT, a specialized LLM for TCM that integrates vertical domain fine-tuning strategies with cognitive psychology insights. Our approach incorporates Multi-TCM Scenario and Knowledge Instruction Construction Strategies, enhanced by Symptom Sequence-based Beam Search and a Medical Finite State Machine (MedicalFSM) module. Using only LoRA fine-tuning, ZhongJingGPT achieves state-of-the-art accuracy, surpassing GPT-4 in key TCM-specific accuracy and fluency metrics. Comprehensive evaluations, including out-of-distribution assessments, renowned TCM practitioners’ case studies, and multi-turn role-playing scenarios, verify its superior performance on Chinese Massive Multitask Language Understanding (CMMLU) and TCM Humanities datasets. A multi-dimensional evaluation standard, assessed by professional practitioners, further validates its effectiveness. This research demonstrates the potential of specialized LLMs in TCM and offers insights for AI development in complex professional domains, bridging ancient wisdom with modern AI technologies.}
}