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
this https URL to facilitate further research in the
domain of traditional Chinese medicine and LLMs.

使用生成型的大型语言模型，在中医问答领域表现令人不满意，本研究引入了一个专门为中医问答设计的对话模型 MedChatZH，该模型在中医经典著作上进行预训练，并结合医学指南数据进行精细调优，在真实的医学对话数据集上胜过了几个基线模型。我们在链接上发布了我们的模型、代码和数据集，以促进传统中医和大型语言模型领域的进一步研究。