AI RESEARCH
Locate-then-Sparsify: Attribution Guided Sparse Strategy for Visual Hallucination Mitigation
arXiv CS.LG
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ArXi:2603.16284v1 Announce Type: cross Despite the significant advancements in Large Vision-Language Models (LVLMs), their tendency to generate hallucinations undermines reliability and restricts broader practical deployment. Among the hallucination mitigation methods, feature steering emerges as a promising approach that reduces erroneous outputs in LVLMs without increasing inference costs. However, current methods apply uniform feature steering across all layers.