Finding the Correct Visual Evidence Without Forgetting: Mitigating Hallucination in LVLMs via Inter-Layer Visual Attention Discrepancy
arXiv:2605.20965v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have shown remarkable performance on a wide range of vision-language tasks. Despite this progress, they are still prone to hallucination, generating responses that are inconsistent with visual content. In this work, we find that LVLMs tend to hallucinate when they pay insufficient attention to the correct visual evidence and gradually forget it during the generation process. We empirically find that although LVLMs overall attend insufficiently to visual evidence, they exhibit sensitivity to the correct visua
The rapid advancement of Large Vision-Language Models (LVLMs) necessitates addressing their core limitations, such as hallucination, to enable reliable real-world deployment.
Mitigating hallucination directly impacts the trustworthiness and utility of AI systems, particularly those interacting with visual data, which is crucial for widespread adoption across various industries.
This research provides a mechanism to improve the factual groundedness of LVLMs, potentially leading to more accurate and dependable AI applications that integrate visual and linguistic understanding.
- · AI developers
- · Enterprises deploying LVLMs
- · Users of multimodal AI systems
- · Companies with high hallucination rates in their LVLM products
- · Applications requiring high visual fidelity with unchecked LVLMs
LVLMs will become more reliable in interpreting and responding to visual information.
Increased trust in LVLMs could accelerate their integration into sensitive applications like medical diagnostics or autonomous systems.
A higher standard for AI factual integrity will emerge, pushing the entire sector towards more robust, verifiable outputs.
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Read at arXiv cs.AI