Detecting hallucinations in large language models is a critical open problem with significant implications for safety and reliability. While existing hallucination detection methods achieve strong performance in question-answering tasks, they remain less effective on tasks requiring reasoning. In this work, we revisit hallucination detection through the lens of out-of-distribution (OOD) detection, a well-studied problem in areas like computer vision. Treating next-token prediction in language models as a classification task allows us to apply OOD techniques, provided appropriate modifications are made to account for the structural differences in large language models. We show that OOD-based approaches yield training-free, single-sample-based detectors, achieving strong accuracy in hallucination detection for reasoning tasks. Overall, our work suggests that reframing hallucination detection as OOD detection provides a promising and scalable pathway toward language model safety.

翻译：检测大型语言模型中的幻觉是一个关键且尚未解决的开放性问题，对安全性和可靠性具有重大影响。尽管现有的幻觉检测方法在问答任务中表现出色，但在需要推理的任务上效果仍然欠佳。在本研究中，我们通过分布外检测的视角重新审视幻觉检测问题，后者是计算机视觉等领域中一个被深入研究的问题。将语言模型中的下一词预测视为分类任务，使我们能够应用OOD检测技术，前提是进行适当修改以考虑大型语言模型的结构差异。我们证明，基于OOD的方法可以产生无需训练、基于单样本的检测器，在推理任务的幻觉检测中实现高准确率。总体而言，我们的研究表明，将幻觉检测重新定义为OOD检测，为语言模型安全性提供了一条有前景且可扩展的途径。