title: Fixing Imbalanced Attention to Mitigate In-Context Hallucination of
    Large Vision-Language Model
summary: 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.
paper_authors._id: 67914269013258a9daeec92a
paper_authors.name: Kazi Hasan Ibn Arif
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-01-22T19:35:08.167Z
paper_authors._id: 654af6f173416a223f5eacf5
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/FZvER_MtUz_BitTVf2QfV.jpeg
paper_authors.fullname: Hasan Arif
paper_authors.type: user
paper_authors.user: hasanar1f
paper_authors._id: 67914269013258a9daeec92b
paper_authors.name: Sajib Acharjee Dip
paper_authors._id: 67914269013258a9daeec92c
paper_authors.name: Khizar Hussain
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-01-22T19:24:54.932Z
paper_authors._id: 6719304706ed69d813c17f72
paper_authors.avatarUrl: /avatars/516e6dc12c1b8de3e8c3bd26207013a3.svg
paper_authors.fullname: Khizar Husasin
paper_authors.type: user
paper_authors.user: KhizarH
paper_authors._id: 67914269013258a9daeec92d
paper_authors.name: Lang Zhang
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-01-22T22:27:51.230Z
paper_authors._id: 677d52aeb04a44ce6218c6ab
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/O_co9rCqruMMlyoqXn1vS.png
paper_authors.fullname: Lang Zhang
paper_authors.type: user
paper_authors.user: Lang2000
paper_authors._id: 67914269013258a9daeec92e
paper_authors.name: Chris Thomas
paper_title: Fixing Imbalanced Attention to Mitigate In-Context Hallucination of
    Large Vision-Language Model
paper_summary: 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.
paper_ai_keywords: Large Vision Language Models (LVLMs) hallucination behavior attention patterns transformer layers heads progressive degradation visual grounding dual-stream token selection mechanism locally informative spatially significant visual tokens attention head-specific modulation strategy visual information processing visual sensitivity MSCOCO dataset hallucinations task performance visual grounding