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Generative large language models (LLMs) have shown great success in variousapplications, including question-answering (QA) and dialogue systems. However,in specialized domains like traditional Chinese medical QA, these models mayperform unsatisfactorily without fine-tuning on domain-specific datasets. Toaddress this, we introduce MedChatZH, a dialogue model designed specificallyfor traditional Chinese medical QA. Our model is pre-trained on Chinesetraditional medical books and fine-tuned with a carefully curated medicalinstruction dataset. It outperforms several solid baselines on a real-worldmedical dialogue dataset. We release our model, code, and dataset onhttps://github.com/tyang816/MedChatZH to facilitate further research in thedomain of traditional Chinese medicine and LLMs.

MedChatZH: a Better Medical Adviser Learns from Better Instructions