Hugging Face Trending Papers
Fine-Grained Multi Image Object Hallucination Benchmark
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arXiv AI
3d ago
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 Computer Vision
2d ago
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.
arXiv Computer Vision
1d ago
Does Playing it Safe Count as Faithfulness? Reassessing LVLM Hallucination Mitigation Methods
arXiv:2609.01888v1 Announce Type: new Abstract: Recent inference-time hallucination mitigation methods for large vision-language models (LVLMs) report strong gains on hallucination benchmarks. Howeve...