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

TL;DR

This paper proposes a novel approach to hallucination detection in large language models by framing it as an out-of-distribution detection problem, enabling training-free and scalable safety solutions.

Contribution

It introduces a geometric perspective that adapts OOD detection techniques to language models, improving hallucination detection especially in reasoning tasks.

Findings

OOD-based detectors achieve high accuracy in hallucination detection

Training-free, single-sample detection methods are effective

Reframing hallucination detection as OOD detection enhances scalability and safety

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.