PSRD mitigates visual hallucinations in LVLMs via phase-wise self-reward decoding, cutting rates by 50% on LLaVA-1.5-7B and outperforming prior methods on five benchmarks.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
MESA reduces hallucinations in LVLMs via controlled selective latent intervention that preserves the original token distribution.
A training-free region-aware attention recalibration strategy reduces object hallucinations in LVLMs on CHAIR, POPE, and MME benchmarks while preserving fluency.
citing papers explorer
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Mitigating Multimodal Hallucination via Phase-wise Self-reward
PSRD mitigates visual hallucinations in LVLMs via phase-wise self-reward decoding, cutting rates by 50% on LLaVA-1.5-7B and outperforming prior methods on five benchmarks.
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Mitigating Entangled Steering in Large Vision-Language Models for Hallucination Reduction
MESA reduces hallucinations in LVLMs via controlled selective latent intervention that preserves the original token distribution.
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Mitigating Object Hallucinations in Vision-Language Models through Region-Aware Attention Recalibration
A training-free region-aware attention recalibration strategy reduces object hallucinations in LVLMs on CHAIR, POPE, and MME benchmarks while preserving fluency.