Large vision-language models often exhibit object hallucination, generating claims that are not supported by the input image. To mitigate this issue, we propose Evidence-Guided Visual Claim Admission...
arXiv:2607. 04163v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have achieved remarkable progress in visual understanding tasks such as image captioning and visual question answering.
arXiv:2608.21819v1 Announce Type: cross Abstract: Reliable image captioning in Vision-Language Models (VLMs) requires captions to be both precise and complete, avoiding unsupported object mentions wh...
arXiv:2608. 04496v1 Announce Type: cross Abstract: Visual inputs in vision-language models (VLMs) are often encoded into substantially longer token sequences than text, making visual tokens a major bottleneck for efficient inference.
The paper investigates why long image captions often fail, arguing that failure depends not just on what facts are mentioned but also on where they appear in the caption. It introduces a position‑aware rubric that breaks down dense human references into atomic visual claims, evaluates generated captions against these claims, and identifies two distinct failure modes: unsupported specificity in the middle and vague, non‑evidential language at the end. Building on this diagnosis, the authors propose PAGER, a reinforcement‑learning objective that provides separate rewards for supported middle evidence, evidence‑bearing tail continuation, claim coverage, and length control, leading to improved position‑wise grounding and caption preference on detailed‑caption benchmarks.
arXiv:2603. 21693v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) have shown strong potential for medical Visual Question Answering (VQA), yet they remain prone to hallucinations, defined as generating responses that contradict the input image, posing serious risks in clinical settings.
ReWEIGH the Evidence is a training‑free decoding technique that calibrates token‑level ordinal visual evidence to reduce hallucinations in large vision‑language models. It aggregates vocabulary ranks across visual positions, compares candidates to a token‑specific reference derived from unlabeled images, and applies a bounded penalty only when evidence falls below this reference. Experiments on four 7B backbones show up to a 21.3% reduction in hallucinated object mentions while largely preserving or improving descriptive and general performance, with minimal added latency.
The paper introduces a position‑aware rubric that breaks down long image captions into atomic visual claims, allowing verification of generated text against these claims and revealing distinct failure modes: unsupported specificity in the middle and vague language at the end. Using this diagnosis, the authors propose PAGER, a reinforcement learning objective that provides separate rewards for middle evidence, tail continuation, claim coverage, and length control. Experiments on detailed‑caption benchmarks show that PAGER improves position‑wise grounding and pairwise caption preference while maintaining overall caption quality.
arXiv:2608.29092v1 Announce Type: new Abstract: Large vision-language models (LVLMs) frequently generate content unsupported by visual inputs. Preliminary experiments show that visual evidence is pri...
arXiv:2605. 01733v3 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) hallucinate objects that are not present, and a growing line of work tries to curb this by feeding the model its own generated caption as auxiliary evidence -- assuming that a caption, once available, is something to consume.
Multimodal large language models (MLLMs) have made strong progress on visual question answering and image captioning, yet they still produce fluent claims about objects, attributes, or relations that...
arXiv:2608.30480v1 Announce Type: cross Abstract: Object hallucination remains a persistent reliability issue in large vision-language models, where generated object mentions may sound plausible but...