arXiv Computer Vision
VIGIL: Tackling Hallucination Detection in Image Recontextualization
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Fine-Grained Multi Image Object Hallucination Benchmark
The paper introduces MIOH, a fine‑grained benchmark for evaluating object hallucination in multimodal large language models (MLLMs) across multiple images. It assesses hallucination through four tasks—existence, counting, attribute, position—using three reasoning patterns (comprehensive, comparative, selective) and three adversarial pressures (visual context scale, perceptual difficulty, contextual bias). Evaluation of 29 models, including GPT‑5 and Gemini‑2.5‑Pro, shows distinct failure patterns, indicating that hallucination arises from integration‑stage limitations rather than just perceptual errors.
Hugging Face Trending Papers
Aug 31
Fine-Grained Multi Image Object Hallucination Benchmark
Multimodal Large Language Models (MLLMs) are increasingly deployed in multi-image scenarios requiring complex reasoning across visual contexts. However, current MLLMs remain fundamentally limited by o...
arXiv AI
Jun 11
MultiToP: Learning to Patch Visual Tokens to Mitigate Hallucinations in Video Large Multimodal Models
arXiv:2606. 11792v1 Announce Type: cross Abstract: Video Large Multimodal Models have achieved remarkable progress in video understanding, yet they remain prone to hallucinations, where generated responses are not faithfully supported by the input video.
arXiv AI
Sep 15
Hallucination in Multimodal Foundation Models: A Survey on Causes, Corrections, and Evaluations
The article surveys hallucination issues in Large Vision‑Language Models (LVLMs), a type of multimodal foundation model that blends visual data with large language models. It categorizes hallucination causes into model architecture and data quality, presents a taxonomy of mitigation strategies, and critically evaluates existing evaluation benchmarks from both discriminative and generative viewpoints. The survey also outlines open challenges and future research directions to improve LVLM reliability and trustworthiness.
arXiv AI
Aug 11
TGIF: Text-Guided Layer Fusion Mitigates Hallucination in Multimodal LLMs
arXiv:2601. 03100v3 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) typically rely on a single late-layer feature from a frozen vision encoder, leaving the encoder's rich hierarchy of visual cues under-utilized.
arXiv Machine Learning
Aug 12
UniProbe: A Learnable Token-Level Hallucination Detector for Large VLMs using Multi-Structural Internal Representations
arXiv:2608. 10835v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) achieve impressive visual reasoning and dialogue capabilities, yet frequently hallucinate content unsupported by the visual input.