GEASS selectively gates and weights self-generated captions using confidence and entropy to reduce object hallucinations in VLMs, outperforming vanilla inference and contrastive decoding on POPE and HallusionBench.
arXiv preprint arXiv:2502.03628 , year=
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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.
Decoder-based VLMs hallucinate due to geometric over-alignment of visual embeddings with the text manifold in a universal dataset-agnostic subspace, mitigated by projecting out the linguistic bias.
ACE uses adversarial counter-commonsense perturbations on image tokens during decoding to suppress hallucinated linguistic priors while preserving stable visual signals in MLLMs.
The survey organizes causes of hallucinations in MLLMs, reviews evaluation benchmarks and metrics, and outlines mitigation approaches plus open questions.
Steering is positioned as a distinct adaptation paradigm that uses targeted activation interventions for local, reversible behavioral changes without parameter updates.
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Hallucination of Multimodal Large Language Models: A Survey
The survey organizes causes of hallucinations in MLLMs, reviews evaluation benchmarks and metrics, and outlines mitigation approaches plus open questions.