AIBullisharXiv – CS AI · Apr 106/10
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Countering the Over-Reliance Trap: Mitigating Object Hallucination for LVLMs via a Self-Validation Framework
Researchers propose a Self-Validation Framework to address object hallucination in Large Vision Language Models (LVLMs), where models generate descriptions of non-existent objects in images. The training-free approach validates object existence through language-prior-free verification and achieves 65.6% improvement on benchmark metrics, suggesting a novel path to enhance LVLM reliability without additional training.