Generate, but Verify: Reducing Hallucination in Vision-Language Models with Retrospective Resampling
This addresses the issue of unreliable outputs in safety-critical applications for users of VLMs, representing a strong specific gain rather than a foundational breakthrough.
The paper tackles the problem of visual hallucinations in Vision-Language Models by introducing REVERSE, a framework that integrates hallucination-aware training with on-the-fly self-verification, achieving state-of-the-art hallucination reduction with improvements of up to 12% on CHAIR-MSCOCO and 34% on HaloQuest.
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 34% on HaloQuest. Our dataset, model, and code are available at: https://reverse-vlm.github.io.