Hugging Face Trending Papers
Fine-Grained Multi Image Object Hallucination Benchmark
Read the original on Hugging Face Trending Papers →The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
Related stories
arXiv AI
Sep 1
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.
arXiv AI
Jul 9
HIVE: Understanding Post-Hallucination Reasoning in Vision Language Models
arXiv:2607. 07507v1 Announce Type: cross Abstract: Hallucinations in vision language models (VLMs) are commonly treated as semantic errors, yet they often arise from partial or ambiguous visual evidence.
arXiv Computation and Language
Sep 11
OmniHallu: Unified Hallucination Detection for Cross-Modal Comprehension and Generation in Multimodal Large Language Models
OmniHallu is a unified framework for detecting hallucinations in multimodal large language models across both comprehension and generation tasks involving image, video, and audio modalities. It introduces OmniHallu-Bench, a 10,000-sample benchmark with claim-level human annotations for six cross-modal tasks (I2T, V2T, A2T, T2I, T2V, T2A). The system uses a multi‑agent architecture that decomposes outputs into atomic claims, verifies them with modality‑specific experts, and aggregates evidence through structured reasoning, while a preference‑optimized verifier reduces expert calls by 66% with minimal performance loss.
arXiv Computer Vision
Sep 2
Beyond Language Priors: Diagnosing and Fixing Visual-Origin Hallucinations in Multimodal LLM
arXiv:2609.00231v1 Announce Type: new Abstract: Existing research on object hallucination in multimodal large language models (MLLMs) predominantly attributes the problem to language priors such as o...
arXiv AI
Aug 12
Grounded Post-Training with Hard Examples for Reducing Hallucination in Multimodal Large Language Models
arXiv:2605. 16411v3 Announce Type: replace-cross Abstract: Hallucination remains a fundamental challenge in vision-language models (VLMs), where autoregressive generation may produce linguistically plausible yet physically inconsistent or visually ungrounded responses due to likelihood maximization under joint probabilistic modeling.
arXiv AI
Aug 7
Reducing Hallucination in Vision-Language Models via Stage-wise Preference Optimization under Distribution Shift
arXiv:2605. 16411v2 Announce Type: replace-cross Abstract: Hallucination remains a fundamental challenge in vision-language models (VLMs), where autoregressive generation may produce linguistically plausible yet physically inconsistent or visually ungrounded responses due to likelihood maximization under joint probabilistic modeling.