Generate, but Verify: Reducing Hallucination in Vision-Language Models
  with Retrospective Resampling
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.
6801bcd484335da5c3e32d0b
Tsung-Han Wu
claimed_verified
2025-04-18T09:38:59.626Z
644a767044b75fd95805d232
https://cdn-avatars.huggingface.co/v1/production/uploads/644a767044b75fd95805d232/vHA2vI_B3CpXapdBEwspB.jpeg
Patrick (Tsung-Han) Wu
user
tsunghanwu
6801bcd484335da5c3e32d0c
Heekyung Lee
claimed_verified
2025-06-14T06:24:56.656Z
677c834f3acf9d7d141df32a
/avatars/4b0b46ed4ee914a46df985e69105e0bf.svg
Heekyung Lee
user
Kyunnilee
6801bcd484335da5c3e32d0d
Jiaxin Ge
6801bcd484335da5c3e32d0e
Joseph E. Gonzalez
6801bcd484335da5c3e32d0f
Trevor Darrell
6801bcd484335da5c3e32d10
David M. Chan
Generate, but Verify: Reducing Hallucination in Vision-Language Models
  with Retrospective Resampling
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.
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.
Vision-Language Models hallucination mitigation generation adjustment post-hoc verification hallucination-aware training self-verification retrospective resampling CHAIR-MSCOCO HaloQuest