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.

翻译：大型语言模型（LLM）结合检索技术（即检索增强生成，RAG）已在开放域问答中取得优异性能。然而，RAG仍存在幻觉问题：模型内部知识及检索上下文的误差可能导致事实性错误输出。现有缓解幻觉的方法常混淆事实性与对检索证据的忠诚度，将未被检索明确支持但事实正确的表述错误标记为幻觉。本文提出FRANQ——一种面向RAG输出的新型幻觉检测方法。该方法通过判断陈述是否忠实于检索上下文，采用不同不确定性量化（UQ）技术分别估计事实性。为评估FRANQ及对比UQ方法，我们构建了新的长文本问答数据集，对事实性与忠诚度进行双重标注，并在复杂案例中结合自动化标注与人工验证。跨多个数据集、任务及大语言模型的广泛实验表明，相较现有方法，FRANQ能更精准检测RAG生成响应中的事实错误。