SeeMe: Mitigating Hallucinations in Large Vision-Language Models through Effective Visual Token Engineering
arXiv:2607.04163v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have achieved remarkable progress in visual understanding tasks such as image captioning and visual question answering. However, they remain susceptible to hallucinations, generating content that is inconsistent with the actual visual input. Existing methods primarily intervene at the decoding stage, while overlooking a critical source of hallucinations: irrelevant or noisy visual tokens that mislead the decoding process. To address this issue, we propose SeeMe, a training-free framework that introduces the
The rapid advancement of Large Vision-Language Models (LVLMs) has brought their limitations, particularly hallucinations, to the forefront, necessitating focused research on mitigation strategies.
Improving the reliability and factual consistency of LVLMs is crucial for their broader adoption in critical applications, enhancing user trust and reducing risks associated with misinformation.
This research introduces a novel, training-free approach to address LVLM hallucinations by targeting visual token engineering, potentially offering a more efficient and direct solution than prior decoding-focused methods.
- · AI researchers
- · Developers of LVLM applications
- · Users of AI systems requiring high fidelity
- · Approaches solely focused on decoding-stage hallucination mitigation
LVLMs become more reliable in generating visual descriptions and answering visual questions.
Reduced need for extensive post-processing or human review of LVLM outputs, accelerating deployment.
Enhanced trust in AI systems could unlock new applications in fields sensitive to factual accuracy, such as medical imaging analysis or autonomous systems.
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Read at arXiv cs.AI