Fixing Imbalanced Attention to Mitigate In-Context Hallucination of
  Large Vision-Language Model
Authors: name: Kazi Hasan Ibn Arif, name: Sajib Acharjee Dip, name: Khizar Hussain, name: Lang Zhang, name: Chris Thomas

Large Vision Language Models (LVLMs) have demonstrated remarkable
capabilities in understanding and describing visual content, achieving
state-of-the-art performance across various vision-language tasks. However,
these models frequently exhibit hallucination behavior, where they generate
descriptions containing objects or details absent in the input image. Our work
investigates this phenomenon by analyzing attention patterns across transformer
layers and heads, revealing that hallucinations often stem from progressive
degradation of visual grounding in deeper layers. We propose a novel attention
modification approach that combines selective token emphasis and head-specific
modulation to maintain visual grounding throughout the generation process. Our
method introduces two key components: (1) a dual-stream token selection
mechanism that identifies and prioritizes both locally informative and
spatially significant visual tokens, and (2) an attention head-specific
modulation strategy that differentially amplifies visual information processing
based on measured visual sensitivity of individual attention heads. Through
extensive experimentation on the MSCOCO dataset, we demonstrate that our
approach reduces hallucination rates by up to 62.3\% compared to baseline
models while maintaining comparable task performance. Our analysis reveals that
selectively modulating tokens across attention heads with varying levels of
visual sensitivity can significantly improve visual grounding without requiring
model retraining.