Adversarial Parametric Editing for VLM Hallucination Mitigation
Published:Dec 26, 2025 11:56
•1 min read
•ArXiv
Analysis
This paper addresses the critical problem of hallucination in Vision-Language Models (VLMs), a significant obstacle to their real-world application. The proposed 'ALEAHallu' framework offers a novel, trainable approach to mitigate hallucinations, contrasting with previous non-trainable methods. The adversarial nature of the framework, focusing on parameter editing to reduce reliance on linguistic priors, is a key contribution. The paper's focus on identifying and modifying hallucination-prone parameter clusters is a promising strategy. The availability of code is also a positive aspect, facilitating reproducibility and further research.
Key Takeaways
- •Proposes a novel, trainable framework (ALEAHallu) for mitigating hallucinations in VLMs.
- •Employs an adversarial approach to edit hallucination-prone parameter clusters.
- •Focuses on reducing reliance on linguistic priors and promoting visual feature integration.
- •Demonstrates effectiveness on both generative and discriminative VLM tasks.
- •Provides publicly available code for reproducibility and further research.
Reference
“The ALEAHallu framework follows an 'Activate-Locate-Edit Adversarially' paradigm, fine-tuning hallucination-prone parameter clusters using adversarial tuned prefixes to maximize visual neglect.”