Mitigating Hallucination in Large Vision-Language Models via Adaptive Attention Calibration

Mehrdad Fazli; Bowen Wei; Ahmet Sari; Ziwei Zhu

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arXiv · 2505.21472

Mitigating Hallucination in Large Vision-Language Models via Adaptive Attention Calibration

Mehrdad Fazli·Bowen Wei·Ahmet Sari·Ziwei Zhu

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Abstract

Large vision-language models (LVLMs) achieve impressive performance on multimodal tasks but often suffer from hallucination, and confidently describe objects or attributes not present in the image. Current training-free interventions struggle to maintain accuracy in open-ended and long-form generation scenarios. We introduce the Confidence-Aware Attention Calibration (CAAC) framework to address this challenge by targeting two key biases: spatial perception bias, which distributes attention disproportionately across image tokens, and modality bias, which shifts focus from visual to textual inputs over time. CAAC employs a two-step approach: Visual-Token Calibration (VTC) to balance attention across visual tokens, and Adaptive Attention Re-Scaling (AAR) to reinforce visual grounding guided by the model's confidence. This confidence-driven adjustment ensures consistent visual alignment during generation. Experiments on CHAIR, AMBER, and POPE benchmarks demonstrate that CAAC outperforms baselines, particularly in long-form generations, effectively reducing hallucination.

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Mehrdad Fazli, Bowen Wei, Ahmet Sari, Ziwei Zhu. 2025-05-27. Mitigating Hallucination in Large Vision-Language Models via Adaptive Attention Calibration. https://arxiv.org/abs/2505.21472

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