arXiv AI
Jul 7

SeeMe: Mitigating Hallucinations in Large Vision-Language Models through Effective Visual Token Engineering

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.

By Kai Tang, Jinhao You, Bohua Zhang, Yichen Guo, Yiding Sun, Dongxu Zhang, Chenxi Li, Xiande Huang, Shanghang Zhang
Hugging Face Trending Papers
Oct 22

Where Do Long Captions Fail? Position-Aware Diagnosis and Reinforcement Learning for Detailed Image Captioning

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 AI
Jul 21

Deterministic Hallucination Detection in Medical VQA via Confidence-Evidence Bayesian Gain

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