Prefill-Time Intervention (PTI) reduces hallucinations in large vision-language models by applying a one-time modality-aware steering correction to the initial KV cache at the prefill stage rather than during autoregressive decoding.
Cai: Caption-sensitive attention in- tervention for mitigating object hallucination in large vision- language models
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CAST reduces object hallucination in LVLMs by 6.03% on average across five models and five benchmarks by identifying caption-sensitive attention heads and applying optimized steering directions to their outputs, with negligible added inference cost.
GEASS adaptively gates and weights self-generated captions in VLMs using confidence, entropy reduction, and pathway disagreement to reduce hallucination and improve benchmark scores.
citing papers explorer
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Prefill-Time Intervention for Mitigating Hallucination in Large Vision-Language Models
Prefill-Time Intervention (PTI) reduces hallucinations in large vision-language models by applying a one-time modality-aware steering correction to the initial KV cache at the prefill stage rather than during autoregressive decoding.
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CAST: Mitigating Object Hallucination in Large Vision-Language Models via Caption-Guided Visual Attention Steering
CAST reduces object hallucination in LVLMs by 6.03% on average across five models and five benchmarks by identifying caption-sensitive attention heads and applying optimized steering directions to their outputs, with negligible added inference cost.
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GEASS: Gated Evidence-Adaptive Selective Caption Trust for Vision-Language Models
GEASS adaptively gates and weights self-generated captions in VLMs using confidence, entropy reduction, and pathway disagreement to reduce hallucination and improve benchmark scores.