从分布外检测到幻觉检测：一个几何视角
From Out-of-Distribution Detection to Hallucination Detection: A Geometric View
AI总结：
本文通过将幻觉检测重新定义为分布外检测问题，利用几何视角提出了一种无需训练、基于单样本的检测方法，在推理任务中实现了高准确率。
摘要：
检测大型语言模型中的幻觉是一个关键且开放的问题，对安全性和可靠性有重大影响。虽然现有的幻觉检测方法在问答任务中表现强劲，但在需要推理的任务上效果不佳。在这项工作中，我们通过分布外（OOD）检测的视角重新审视幻觉检测，这是计算机视觉等领域中一个研究充分的问题。将语言模型中的下一个词预测视为分类任务，允许我们应用OOD技术，前提是进行适当的修改以考虑大型语言模型的结构差异。我们表明，基于OOD的方法产生了无需训练、基于单样本的检测器，在推理任务的幻觉检测中实现了高准确率。总体而言，我们的工作表明，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路径。
英文摘要：
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.
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。
