将幻觉检测重新定义为分布外检测，提升大模型推理任务的可靠性。
From Out-of-Distribution Detection to Hallucination Detection: A Geometric View
- 把语言模型的下一步词预测看作分类问题，用分布外检测方法识别幻觉。
- 无需训练、单样本即可检测，推理任务中幻觉检测准确率显著提升。
- 适合关注大模型安全与可靠性的研究者，尤其在复杂推理场景下。
大语言模型中的幻觉检测是一个关键且开放的问题，对安全性与可靠性具有重要影响。尽管现有幻觉检测方法在问答任务中表现良好，但在需要推理的任务上效果仍不理想。本文从分布外（OOD）检测的视角重新审视幻觉检测，这一问题在计算机视觉等领域已有深入研究。通过将语言模型的下一步词预测视为分类任务，可应用OOD检测技术，只需针对大模型的结构差异进行适当调整。实验表明，基于OOD的方法能实现无需训练、仅需单样本的检测器，在推理任务中表现出优异的幻觉检测准确性。整体而言，将幻觉检测重构为分布外检测，为提升语言模型安全性提供了一条有前景且可扩展的路径。
原文摘要 · Abstract (English)
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.
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