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IBD: Alleviating Hallucinations in Large Vision-Language Models via Image-Biased Decoding

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arxiv 2402.18476 v1 pith:WISR7F3G submitted 2024-02-28 cs.CV

Lanyun Zhu , Deyi Ji , Tianrun Chen , Peng Xu , Jieping Ye , Jun Liu This is my paper
classification cs.CV
keywords hallucinationsimage-biasedmethoddecodingdespitelargelvlmlvlms
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Despite achieving rapid developments and with widespread applications, Large Vision-Language Models (LVLMs) confront a serious challenge of being prone to generating hallucinations. An over-reliance on linguistic priors has been identified as a key factor leading to these hallucinations. In this paper, we propose to alleviate this problem by introducing a novel image-biased decoding (IBD) technique. Our method derives the next-token probability distribution by contrasting predictions from a conventional LVLM with those of an image-biased LVLM, thereby amplifying the correct information highly correlated with image content while mitigating the hallucinatory errors caused by excessive dependence on text. We further conduct a comprehensive statistical analysis to validate the reliability of our method, and design an adaptive adjustment strategy to achieve robust and flexible handling under varying conditions. Experimental results across multiple evaluation metrics verify that our method, despite not requiring additional training data and only with a minimal increase in model parameters, can significantly reduce hallucinations in LVLMs and enhance the truthfulness of the generated response.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens

    cs.CV 2025-09 conditional novelty 7.0 of 10

    Feed-forward neurons in LVLMs encode whether a text token is visually grounded, and a detector built on these neurons can reduce hallucination by overriding or replacing ungrounded tokens.

  2. CAI: Caption-Sensitive Attention Intervention for Mitigating Object Hallucination in Large Vision-Language Models

    cs.CV 2025-06 conditional novelty 5.0 of 10

    CAI reduces object hallucination in LVLMs by injecting caption-query attention patterns into selected attention heads at inference time.

  3. PostAlign: Multimodal Grounding as a Corrective Lens for MLLMs

    cs.CV 2025-06 conditional novelty 5.0 of 10

    MMGrounded-PostAlign trains MLLMs to produce a grounded object token or a rejection token plus selective rationales, improving hallucination and VQA benchmarks.

  4. MCA-LLaVA: Manhattan Causal Attention for Reducing Hallucination in Large Vision-Language Models

    cs.CV 2025-07 conditional novelty 4.0 of 10

    MCA-LLaVA reindexes image tokens by sums of mirrored 2D coordinates so instruction tokens attend across the whole image, reducing hallucination on POPE, CHAIR, and MME.

  5. Mitigating Behavioral Hallucination in Multimodal Large Language Models for Sequential Images

    cs.AI 2025-06 reject novelty 4.0 of 10

    SHE lowers behavioral hallucination scores by about 10 percent by detecting low visual-textual similarity and projecting out the hallucinated direction in embedding space.

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