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

## TL;DR

XuanHuGPT is a specialized AI model for Traditional Chinese Medicine, trained with a new dataset and efficient tuning methods to improve accuracy and performance.

## Contribution

A novel parameter-efficient fine-tuning approach for TCM-specific LLMs using a structured dataset and comprehensive evaluation framework.

## Key findings

- XuanHuGPT outperforms general and TCM-specific models in accuracy, coverage, and fluency.
- The XhTCM dataset with 100,000 entries enhances model training for TCM tasks.
- PEFT techniques effectively balance performance and training costs for domain-specific models.

## 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.