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.

翻译：大语言模型（LLMs）在通用领域的多种自然语言处理（NLP）任务中已展现出显著成功。然而，由于领域知识有限，LLMs有时会生成包含医学事实幻觉的响应。此类缺陷在医疗场景中应用LLMs时带来了潜在风险。为解决这一挑战，我们提出知识调优方法，利用结构化医学知识库使LLMs高效掌握领域知识，并促进可靠响应的生成。我们还发布了cMedKnowQA，一个基于医学知识库构建的中文医学知识问答数据集，用于评估LLMs的医学知识掌握水平。实验结果表明，与标准指令调优相比，经cMedKnowQA知识调优后的LLMs在响应生成中展现出更高的准确性，为LLMs的领域适配提供了一种新的可靠途径。