文献类型：期刊文献

英文题名：MedChatZH: A tuning LLM for traditional Chinese medicine consultations

作者：Tan, Yang[1,3,4]; Zhang, Zhixing[2]; Li, Mingchen[1,3,4]; Pan, Fei[2]; Duan, Hao[2]; Huang, Zijie[1]; Deng, Hua[2]; Yu, Zhuohang[2]; Yang, Chen[2]; Shen, Guoyang[4]; Qi, Peng[4]; Yue, Chengyuan[2]; Liu, Yuxian[5]; Hong, Liang[3,4,6]; Yu, Huiqun[1]; Fan, Guisheng[1]; Tang, Yun[2]

机构：[1] Department of Computer Science and Technology, East China University of Science and Technology, Shanghai, 200237, China; [2] Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China; [3] Shanghai Artificial Intelligence Laboratory, Shanghai, 200240, China; [4] Chongqing Artificial Intelligence Research Institute of Shanghai Jiao Tong University, 200240, China; [5] The University of Sydney, Sydney, 2050, Australia; [6] School of Physics and Astronomy & School of Pharmacy, Shanghai Jiao Tong University, Shanghai, 200240, China

年份：2024

卷号：172

外文期刊名：Computers in Biology and Medicine

收录：EI(收录号：20241215781627)

语种：英文

外文关键词：Computational linguistics - Medicine - Speech processing

摘要：Generative Large Language Models (LLMs) have achieved significant success in various natural language processing tasks, including Question-Answering (QA) and dialogue systems. However, most models are trained on English data and lack strong generalization in providing answers in Chinese. This limitation is especially evident in specialized domains like traditional Chinese medical QA, where performance suffers due to the absence of fine-tuning and high-quality datasets. To address this, we introduce MedChatZH, a dialogue model optimized for Chinese medical QA based on transformer decoder with LLaMA architecture. Continued pre-training on a curated corpus of Chinese medical books is followed by fine-tuning with a carefully selected medical instruction dataset, resulting in MedChatZH outperforming several Chinese dialogue baselines on a real-world medical dialogue dataset. Our model, code, and dataset are publicly available on GitHub (https://github.com/tyang816/MedChatZH) to encourage further research in traditional Chinese medicine and LLMs. ? 2024 Elsevier Ltd