Work

XuanHuGPT: parameter-efficient fine-tuning of large language model in the field of traditional Chinese medicine

Year: 2025

Type: article

Abstract: Large Language Models (LLMs) have demonstrated exceptional generalization capabilities across various fields, including their application in Traditional Chinese Medicine (TCM). However, the performance of existing LLMs in TCM-specific tasks remains limited due to the lack of optimization for TCM knowledge during the pre-training phase, insufficient datasets, and the constraints of fine-tuning techniques. To address these challenges, this study constructs the XhTCM dataset by systematically integrating data from three authoritative sources-ShenNong_TCM_Dataset, TCMBank, and TCMIP v2.0. The dataset includes 100,000 structured entries, covering classical theories, prescription formulations, herbal pharmacology, and modern clinical practices. Based on this, we present XuanHuGPT, a domain-specific LLM tailored for TCM question answering and inference. By applying Parameter-Efficient Fine-Tuning (PEFT) techniques, we effectively balance model performance and training costs. Furthermore, we establish a comprehensive evaluation framework for TCM LLMs, combining quantitative metrics (BLEU, ROUGE, METEOR, BERTScore, and Embedding Distance) with expert qualitative assessments. Experimental results show that XuanHuGPT significantly outperforms both general-purpose LLMs and some existing TCM-specific models in accuracy, coverage, fluency, consistency, sensitivity, and safety. This study presents a reproducible paradigm for building intelligent TCM Q&A systems, contributing to the digital transformation, intelligent development, and global dissemination of TCM knowledge.

Source: Chinese Medicine

DOI: 10.1186/s13020-025-01200-3