[Paper] HaloProbe: Bayesian Detection and Mitigation of Object Hallucinations in Vision-Language Models

Summary

Large vision-language models frequently produce object hallucinations in image descriptions, highlighting a need for improved detection. This paper reveals that common detection strategies relying on coarse-grained attention weights on visual tokens are unreliable. It identifies hidden confounders like token position and object repetition that distort attention trends, leading to phenomena like Simpson's paradox where expected patterns reverse or vanish.

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