arXiv:2606. 03376v2 Announce Type: replace-cross Abstract: Hallucination has recently garnered significant research attention in Large Vision-Language Models (LVLMs).
By Ruipeng Zhang, Zhihao Li, Haozhang Yuan, C. L. Philip Chen, Tong Zhang
arXiv:2606. 03376v1 Announce Type: cross Abstract: Hallucination has recently garnered significant research attention in Large Vision-Language Models (LVLMs).
By Ruipeng Zhang, Zhihao Li, Haozhang Yuan, C. L. Philip Chen, Tong Zhang
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.
By Jihae Jeong, Junha Choi, Hwanjo Yu
arXiv:2609.26093v1 Announce Type: new
Abstract: Vision-language models can answer spatial relation questions confidently even when the image supports an incompatible relation. We formulate relation-g...
By Feixiang Liu, Qiang Qiu, Qingyang Li, Hui Xu
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: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.
By Mohammad Asadi, Tahoura Nedaee, Jack W. O'Sullivan, Euan Ashley, Ehsan Adeli