基于事实性的不确定性量化用于验证检索增强生成的输出
Faithfulness-Aware Uncertainty Quantification for Fact-Checking the Output of Retrieval Augmented Generation
AI总结：
本文提出FRANQ方法，通过不同不确定性量化技术区分事实性和检索忠实性，提升RAG输出的事实性检测准确性。
摘要：
大型语言模型（LLMs）通过检索增强（RAG）在开放领域问答中表现优异，但易产生幻觉：事实性错误可能源于模型内部知识或检索上下文的不准确。现有方法常混淆事实性与检索证据的忠实性，错误地将未被检索支持的正确陈述标记为幻觉。本文引入FRANQ，通过不同不确定性量化技术估计事实性，根据陈述是否忠实于检索上下文进行条件判断。为评估FRANQ及竞争UQ方法，我们构建了一个新的长形式问答数据集，结合自动化标注与人工验证挑战案例。跨多个数据集、任务和LLM的实验表明，FRANQ在RAG生成响应中检测事实性错误的准确性优于现有方法。
英文摘要：
Large Language Models (LLMs) enhanced with retrieval, an approach known as Retrieval-Augmented Generation (RAG), have achieved strong performance in open-domain question answering. However, RAG remains prone to hallucinations: factually incorrect outputs may arise from inaccuracies in the model's internal knowledge and the retrieved context. Existing approaches to mitigating hallucinations often conflate factuality with faithfulness to the retrieved evidence, incorrectly labeling factually correct statements as hallucinations if they are not explicitly supported by the retrieval. In this paper, we introduce FRANQ, a new method for hallucination detection in RAG outputs. FRANQ applies distinct uncertainty quantification (UQ) techniques to estimate factuality, conditioning on whether a statement is faithful to the retrieved context. To evaluate FRANQ and competing UQ methods, we construct a new long-form question answering dataset annotated for both factuality and faithfulness, combining automated labeling with manual validation of challenging cases. Extensive experiments across multiple datasets, tasks, and LLMs show that FRANQ achieves more accurate detection of factual errors in RAG-generated responses compared to existing approaches.
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