Large Language Models (LLMs) have demonstrated remarkable success in diverse
natural language processing (NLP) tasks in general domains. However, LLMs
sometimes generate responses with the hallucination about medical facts due to
limited domain knowledge. Such shortcomings pose potential risks in the
utilization of LLMs within medical contexts. To address this challenge, we
propose knowledge-tuning, which leverages structured medical knowledge bases
for the LLMs to grasp domain knowledge efficiently and facilitate reliable
response generation. We also release cMedKnowQA, a Chinese medical knowledge
question-answering dataset constructed from medical knowledge bases to assess
the medical knowledge proficiency of LLMs. Experimental results show that the
LLMs which are knowledge-tuned with cMedKnowQA, can exhibit higher levels of
accuracy in response generation compared with vanilla instruction-tuning and
offer a new reliable way for the domain adaptation of LLMs.

为了解决大型语言模型（LLM）在医学领域中知识局限导致产生虚假医学事实的困境，本研究提出了知识调整的方法，利用结构化的医学知识库帮助 LLMs 有效掌握领域知识，从而实现可靠的回答生成。通过引入基于医学知识库构建的中文医学知识问答数据集 cMedKnowQA，实验结果表明，经过 cMedKnowQA 知识调整的 LLMs 在回答生成上表现出更高的准确性，为 LLMs 的领域适应提供了可靠的新途径。