AI RESEARCH
From Out-of-Distribution Detection to Hallucination Detection: A Geometric View
arXiv CS.AI
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ArXi:2602.07253v2 Announce Type: replace 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.