title: Generate, but Verify: Reducing Hallucination in Vision-Language Models
    with Retrospective Resampling
summary: Vision-Language Models (VLMs) excel at visual understanding but often suffer
  from visual hallucinations, where they generate descriptions of nonexistent
  objects, actions, or concepts, posing significant risks in safety-critical
  applications. Existing hallucination mitigation methods typically follow one of
  two paradigms: generation adjustment, which modifies decoding behavior to align
  text with visual inputs, and post-hoc verification, where external models
  assess and correct outputs. While effective, generation adjustment methods
  often rely on heuristics and lack correction mechanisms, while post-hoc
  verification is complicated, typically requiring multiple models and tending to
  reject outputs rather than refine them. In this work, we introduce REVERSE, a
  unified framework that integrates hallucination-aware training with on-the-fly
  self-verification. By leveraging a new hallucination-verification dataset
  containing over 1.3M semi-synthetic samples, along with a novel inference-time
  retrospective resampling technique, our approach enables VLMs to both detect
  hallucinations during generation and dynamically revise those hallucinations.
  Our evaluations show that REVERSE achieves state-of-the-art hallucination
  reduction, outperforming the best existing methods by up to 12% on CHAIR-MSCOCO
  and 28% on HaloQuest. Our dataset, model, and code are available at:
  https://reverse-vlm.github.io.
paper_authors._id: 6801bcd484335da5c3e32d0b
paper_authors.name: Tsung-Han Wu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-18T09:38:59.626Z
paper_authors._id: 644a767044b75fd95805d232
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/644a767044b75fd95805d232/vHA2vI_B3CpXapdBEwspB.jpeg
paper_authors.fullname: Patrick (Tsung-Han) Wu
paper_authors.type: user
paper_authors.user: tsunghanwu
paper_authors._id: 6801bcd484335da5c3e32d0c
paper_authors.name: Heekyung Lee
paper_authors._id: 6801bcd484335da5c3e32d0d
paper_authors.name: Jiaxin Ge
paper_authors._id: 6801bcd484335da5c3e32d0e
paper_authors.name: Joseph E. Gonzalez
paper_authors._id: 6801bcd484335da5c3e32d0f
paper_authors.name: Trevor Darrell
paper_authors._id: 6801bcd484335da5c3e32d10
paper_authors.name: David M. Chan
paper_title: Generate, but Verify: Reducing Hallucination in Vision-Language Models
    with Retrospective Resampling
paper_summary: Vision-Language Models (VLMs) excel at visual understanding but often suffer
  from visual hallucinations, where they generate descriptions of nonexistent
  objects, actions, or concepts, posing significant risks in safety-critical
  applications. Existing hallucination mitigation methods typically follow one of
  two paradigms: generation adjustment, which modifies decoding behavior to align
  text with visual inputs, and post-hoc verification, where external models
  assess and correct outputs. While effective, generation adjustment methods
  often rely on heuristics and lack correction mechanisms, while post-hoc
  verification is complicated, typically requiring multiple models and tending to
  reject outputs rather than refine them. In this work, we introduce REVERSE, a
  unified framework that integrates hallucination-aware training with on-the-fly
  self-verification. By leveraging a new hallucination-verification dataset
  containing over 1.3M semi-synthetic samples, along with a novel inference-time
  retrospective resampling technique, our approach enables VLMs to both detect
  hallucinations during generation and dynamically revise those hallucinations.
  Our evaluations show that REVERSE achieves state-of-the-art hallucination
  reduction, outperforming the best existing methods by up to 12% on CHAIR-MSCOCO
  and 28% on HaloQuest. Our dataset, model, and code are available at:
  https://reverse-vlm.github.io.
paper_ai_summary: REVERSE is a unified framework that reduces hallucinations in Vision-Language Models by integrating hallucination-aware training and on-the-fly self-verification using a novel retrospective resampling technique.
paper_ai_keywords: Vision-Language Models hallucination mitigation generation adjustment post-hoc verification hallucination-aware training self-verification retrospective resampling CHAIR-MSCOCO HaloQuest