arXiv:2605.23344v1 Announce Type: cross Abstract: Large Vision-Language Models have shown strong multimodal reasoning capabilities, yet they remain susceptible to object hallucinations when language priors dominate insufficient or misaligned visual evidence. Training-free contrastive decoding methods mitigate this issue by comparing predictions from original and perturbed visual inputs, but existing approaches either apply global perturbations that may alter useful visual evidence or invoke an additional negative branch at every decoding step. In this paper, we observe that hallucination risks
The proliferation of Large Vision-Language Models (LVLMs) has brought hallucination issues to the forefront, necessitating robust mitigation strategies to enhance their reliability and trustworthiness.
Addressing object hallucinations is crucial for the widespread adoption and dependable application of LVLMs across critical domains, as unreliable outputs undermine their utility and safety.
This research introduces a novel training-free method, CHASD, that improves LVLM reliability by tackling hallucination without global perturbations or additional negative branches, offering a more efficient and effective solution.
- · AI developers and researchers
- · Industries relying on multimodal AI (e.g., healthcare, autonomous driving)
- · Users of LVLMs
- · Existing less efficient hallucination mitigation techniques
Improved accuracy and trustworthiness of Large Vision-Language Models in real-world applications.
Accelerated deployment of LVLMs in sensitive and high-stakes environments due to enhanced reliability.
Increased public and institutional confidence in advanced AI systems, potentially fostering broader AI integration.
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Read at arXiv cs.AI