Large language models (LLMs) hold promise for addressing healthcare challenges but often generate hallucinations due to limited integration of medical knowledge. Incorporating external medical knowledge is therefore critical, especially considering the breadth and complexity of medical content, which necessitates effective multi-source knowledge acquisition. We address this challenge by framing it as a source planning problem, where the task is to formulate context-appropriate queries tailored to the attributes of diverse knowledge sources. Existing approaches either overlook source planning or fail to achieve it effectively due to misalignment between the model's expectation of the sources and their actual content. To bridge this gap, we present MedOmniKB, a comprehensive repository comprising multigenre and multi-structured medical knowledge sources. Leveraging these sources, we propose the Source Planning Optimisation (SPO) method, which enhances multi-source utilisation through explicit planning optimisation. Our approach involves enabling an expert model to explore and evaluate potential plans while training a smaller model to learn source alignment using positive and negative planning samples. Experimental results demonstrate that our method substantially improves multi-source planning performance, enabling the optimised small model to achieve state-of-the-art results in leveraging diverse medical knowledge sources.

本研究针对大型语言模型在医疗实践中因医疗知识整合不足而产生的幻觉问题，提出了一个新颖的源规划方法，通过构建一个综合性医疗知识库MedOmniKB，以优化多源利用。我们的实验结果显示，此方法显著提升了多源规划性能，使得优化后的小型模型在利用多样化医疗知识源方面达到了最先进的效果。

迈向全方位检索增强生成：大型语言模型在医疗应用中的综合性研究