FADE: Mitigating Hallucinations by Reducing Language-Prior Dominance in Large Vision-Language Models
arXiv:2606.29431v1 Announce Type: new Abstract: Despite the impressive capabilities of Large Vision-Language Models (LVLMs), they remain susceptible to hallucination, generating content inconsistent with the input image. Recent studies attribute this to the dominance of language priors over visual inputs and employ contrastive decoding methods to mitigate this dominance, but the mechanistic origin remains unexplored. We investigate the information flow through each transformer layer and find that attention modules consistently aggregate visual evidence, while FFN modules at critical layers act
The proliferation of advanced LVLMs highlights the urgency of addressing core architectural limitations like hallucination, driving research into mechanistic origins and mitigation strategies.
Improving the trustworthiness and reliability of AI models, particularly LVLMs, is crucial for their broader adoption in sensitive and critical applications.
This research provides a deeper understanding of how hallucinations occur in LVLMs and proposes a method to mitigate them, potentially leading to more robust and dependable AI systems.
- · AI developers
- · Enterprises deploying LVLMs
- · AI ethics researchers
- · Users of AI applications
- · Unreliable AI systems
- · Developers ignoring hallucination issues
More accurate and trustworthy large vision-language models become available for various applications.
Increased adoption of LVLMs in sectors requiring high reliability, such as healthcare, finance, or highly technical analysis.
Reduced public skepticism towards advanced AI, fostering faster integration into daily life and critical infrastructure.
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Read at arXiv cs.AI