From Out-of-Distribution Detection to Hallucination Detection: A Geometric View

• URL: http://arxiv.org/abs/2602.07253v1
• Date: Fri, 06 Feb 2026 23:05:48 GMT
• Title: From Out-of-Distribution Detection to Hallucination Detection: A Geometric View
• Authors: Litian Liu, Reza Pourreza, Yubing Jian, Yao Qin, Roland Memisevic,
• Abstract summary: We revisit hallucination detection through the lens of out-of-distribution (OOD) detection.<n>Treating next-token prediction in language models as a classification task allows us to apply OOD techniques.<n>We show that OOD-based approaches yield training-free, single-sample-based detectors, achieving strong accuracy in hallucination detection for reasoning tasks.
• Score: 11.026648707364402
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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