Generate, but Verify: Reducing Hallucination in Vision-Language Models
  with Retrospective Resampling
Authors: name: Tsung-Han Wu, name: Heekyung Lee, name: Jiaxin Ge, name: Joseph E. Gonzalez, name: Trevor Darrell, name: David M. Chan

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.