Hallucinations in large language model (LLM) outputs severely limit their reliability in knowledge-intensive tasks such as question answering. To address this challenge, we introduce REFIND (Retrieval-augmented Factuality hallucINation Detection), a novel framework that detects hallucinated spans within LLM outputs by directly leveraging retrieved documents. As part of the REFIND, we propose the Context Sensitivity Ratio (CSR), a novel metric that quantifies the sensitivity of LLM outputs to retrieved evidence. This innovative approach enables REFIND to efficiently and accurately detect hallucinations, setting it apart from existing methods. In the evaluation, REFIND demonstrated robustness across nine languages, including low-resource settings, and significantly outperformed baseline models, achieving superior IoU scores in identifying hallucinated spans. This work highlights the effectiveness of quantifying context sensitivity for hallucination detection, thereby paving the way for more reliable and trustworthy LLM applications across diverse languages.

本文研究了解决大型语言模型输出中的幻觉问题，这些幻觉在知识密集型任务中严重影响其可靠性。提出的REFIND框架利用检索到的文档来检测输出中的幻觉，并引入了上下文敏感性比率（CSR）这一新指标，显示了在多种语言环境下优于现有方法的检测性能，从而提高了大语言模型的信任度。

REFIND：增检索的事实幻觉检测在大语言模型中的应用