通过检索增强的大语言模型中的原子事实核查来提高医疗问答的可靠性与可解释性
Improving Reliability and Explainability of Medical Question Answering through Atomic Fact Checking in Retrieval-Augmented LLMs
AI总结：
通过原子事实核查提升医疗问答的可靠性与可解释性，减少幻觉并提高事实准确性。
摘要：
大型语言模型（LLMs）具有广泛的知识，但容易产生幻觉和不准确的引用，这对它们的临床应用和监管合规性构成了挑战。当前的方法，如检索增强生成，通过将答案与源文档联系起来部分解决了这些问题，但幻觉和低事实层面的可解释性仍然存在。在本工作中，我们介绍了一种新的原子事实核查框架，旨在提高用于医疗长格式问答的LLM的可靠性和可解释性。该方法将LLM生成的响应分解为可验证的离散单元，称为原子事实，每个单元都独立地与权威的医学指南知识库进行验证。这种方法使能够针对错误进行定向修正，并直接追溯到来源文献，从而提高事实准确性和可解释性。通过多读者评估和自动化开放式问答基准的广泛评估，证明了在事实准确性和可解释性方面有显著改进。我们的框架在整体回答改进上达到了最高40%，幻觉检测率为50%。能够追溯每个原子事实到数据库中最相关的片段提供了对生成响应的细粒度、透明的解释，填补了当前医疗AI应用中的主要空白。这项工作是迈向更可信赖和可靠临床应用的重要一步，解决了临床应用的关键前提，并促进了对AI辅助医疗的信心。
英文摘要：
Large language models (LLMs) exhibit extensive medical knowledge but are prone to hallucinations and inaccurate citations, which pose a challenge to their clinical adoption and regulatory compliance. Current methods, such as Retrieval Augmented Generation, partially address these issues by grounding answers in source documents, but hallucinations and low fact-level explainability persist. In this work, we introduce a novel atomic fact-checking framework designed to enhance the reliability and explainability of LLMs used in medical long-form question answering. This method decomposes LLM-generated responses into discrete, verifiable units called atomic facts, each of which is independently verified against an authoritative knowledge base of medical guidelines. This approach enables targeted correction of errors and direct tracing to source literature, thereby improving the factual accuracy and explainability of medical Q&A. Extensive evaluation using multi-reader assessments by medical experts and an automated open Q&A benchmark demonstrated significant improvements in factual accuracy and explainability. Our framework achieved up to a 40% overall answer improvement and a 50% hallucination detection rate. The ability to trace each atomic fact back to the most relevant chunks from the database provides a granular, transparent explanation of the generated responses, addressing a major gap in current medical AI applications. This work represents a crucial step towards more trustworthy and reliable clinical applications of LLMs, addressing key prerequisites for clinical application and fostering greater confidence in AI-assisted healthcare.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
