arXiv:2603.24058v2 Announce Type: replace-cross Abstract: Object hallucination in Large Vision-Language Models (LVLMs) severely compromises their reliability in real-world applications, posing a critical barrier to their deployment in high-stakes scenarios such as autonomous driving and medical image analysis. Through systematic empirical investigation, we identify that the imbalanced attention allocation, both across modalities (i.e., vision and language) and within modalities (among individual tokens), exhibits a strong causal correlation with the occurrence of object hallucination. Leveragi
The rapid deployment of Large Vision-Language Models (LVLMs) into critical applications highlights the increasing urgency to address their inherent reliability issues, particularly object hallucination.
Improving LVLM reliability directly impacts their broader adoption in high-stakes fields like autonomous driving and medical analysis, accelerating their real-world utility and trustworthiness.
This research provides a fundamental understanding and a potential mitigation strategy for a core limitation of LVLMs, enabling more robust and dependable AI applications.
- · AI developers
- · Autonomous vehicle companies
- · Medical AI companies
- · AI safety researchers
- · AI systems prone to hallucination
- · Development teams ignoring foundational reliability
LVLMs become more reliable and less prone to generating incorrect information, improving user trust.
Increased adoption of LVLMs in critical sectors due to enhanced safety and accuracy.
Accelerated development of AI agents that rely on vision and language for decision-making in diverse environments.
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Read at arXiv cs.AI