MedChatZH: a Better Medical Adviser Learns from Better Instructions

• URL: http://arxiv.org/abs/2309.01114v1
• Date: Sun, 3 Sep 2023 08:08:15 GMT
• Title: MedChatZH: a Better Medical Adviser Learns from Better Instructions
• Authors: Yang Tan, Mingchen Li, Zijie Huang, Huiqun Yu and Guisheng Fan
• Abstract summary: 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.
• Score: 11.08819869122466
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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