← 研踪 PaperTrailACL Anthology · ACL 2026:acl:2026.acl-long.1742 · 记录 #1
DiVE: Decoupling Intra-layer Visual Evidence for Mitigating Hallucinations in Large Vision-Language Models
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 21:50
摘要
Recent Large Vision-Language Models (LVLMs) have achieved significant progress yet frequently suffer from visual hallucinations, often stemming from an over-reliance on language priors rather than visual evidence. Existing decoding-based approaches often rely on input perturbations to weaken language priors, but they do not explicitly decouple visual evidence from mixed vision–language representations. To address these limitations, we propose DiVE (Decoupling intra-layer Visual Evidence). DiVE dynamically identifies layers enriched with visual information and performs intra-layer decoupling to extract aggregated visual evidence. By suppressing this evidence to construct a language-prior-dominated reference distribution, DiVE employs contrastive decoding to calibrate the output logits, thereby mitigating hallucinations. Extensive experiments across diverse LVLM architectures demonstrate that DiVE achieves state-of-the-art performance among decoding-based methods on multiple benchmarks. Crucially, it eliminates the latency of an extra forward pass, offering a lightweight and efficient solution.
论文发现出处
这些页面提供论文线索，不代表同行评审或质量认证。
观测到的版本
平台记录号代表本站观测到的元数据变化，不是作者修订号。来源日期仅到月份或年份时，按该月或该年首日存档。
记录 #1 · 2026-07 · 最新观测版本
DiVE: Decoupling Intra-layer Visual Evidence for Mitigating Hallucinations in Large Vision-Language Models
Recent Large Vision-Language Models (LVLMs) have achieved significant progress yet frequently suffer from visual hallucinations, often stemming from an over-reliance on language priors rather than visual evidence. Existing decoding-based approaches often rely on input perturbations to weaken language priors, but they do not explicitly decouple visual evidence from mixed vision–language representations. To address these limitations, we propose DiVE (Decoupling intra-layer Visual Evidence). DiVE dynamically identifies layers enriched with visual information and performs intra-layer decoupling to extract aggregated visual evidence. By suppressing this evidence to construct a language-prior-dominated reference distribution, DiVE employs contrastive decoding to calibrate the output logits, thereby mitigating hallucinations. Extensive experiments across diverse LVLM architectures demonstrate that DiVE achieves state-of-the-art performance among decoding-based methods on multiple benchmarks. Crucially, it eliminates the latency of an extra forward pass, offering a lightweight and efficient solution.
查看此版本解读 →查看此版本原文 ↗