MedChatZH: a Better Medical Adviser Learns from Better Instructions
Yang Tan Mingchen Li Zijie Huang Huiqun Yu Guisheng Fan
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.