Generative large language models (LLMs) have shown great success in various applications, including question-answering (QA) and dialogue systems. However, in specialized domains like traditional Chinese medical QA, these models may perform unsatisfactorily without fine-tuning on domain-specific datasets. To address this, we introduce MedChatZH, a dialogue model designed specifically for traditional Chinese medical QA. Our model is pre-trained on Chinese traditional medical books and fine-tuned with a carefully curated medical instruction dataset. It outperforms several solid baselines on a real-world medical dialogue dataset. We release our model, code, and dataset on https://github.com/tyang816/MedChatZH to facilitate further research in the domain of traditional Chinese medicine and LLMs.

翻译：生成式大语言模型（LLMs）已在包括问答系统和对话系统在内的多种应用中展现出巨大成功。然而，在传统中医问答等专业领域，若未经过领域特定数据集的微调，这些模型的表现可能不尽如人意。为解决这一问题，我们提出了MedChatZH——一种专为传统中医问答设计的对话模型。该模型基于中医古籍文本进行预训练，并通过精心构建的医疗指令数据集进行微调。在真实医疗对话数据集上，其性能优于多个强基线模型。我们已在https://github.com/tyang816/MedChatZH 上公开模型、代码及数据集，以促进传统中医药与大语言模型领域的进一步研究。