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pith:MCYNG54F

pith:2024:MCYNG54FMM74BLASM5OMVQKGDV

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A Survey on Hallucination in Large Vision-Language Models

Dapeng Chen, Hanchao Liu, Ke Wang, Liping Hou, Rongjun Li, Wei Peng, Wenyuan Xue, Xiutian Zhao, Yifei Chen

Large vision-language models generate text that conflicts with input images, and this survey defines the problem while reviewing its symptoms, benchmarks, causes, and fixes.

arxiv:2402.00253 v2 · 2024-02-01 · cs.CV · cs.CL · cs.LG

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Claims

C1strongest claim

Hallucination, defined as the misalignment between factual visual content and corresponding textual generation, poses a significant challenge of utilizing LVLMs, and this survey establishes an overview to facilitate future mitigation.

C2weakest assumption

That hallucinations can be consistently defined and isolated across diverse LVLM architectures and tasks without significant overlap with other error types such as reasoning failures.

C3one line summary

This survey reviews the definition, symptoms, evaluation benchmarks, root causes, and mitigation methods for hallucinations in large vision-language models.

References

56 extracted · 56 resolved · 21 Pith anchors

[1] Flamingo: a visual language model for few-shot learning 2022

[2] Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond 2023 · arXiv:2308.12966

[3] Position-enhanced visual instruction tuning for multimodal large language models

[4] MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning · arXiv:2310.09478

[5] InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks · arXiv:2312.14238

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Cited by

44 papers in Pith

Mitigating Object Hallucinations via Sentence-Level Early Intervention

VidHal: Benchmarking Temporal Hallucinations in Vision LLMs

Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning

Seeing is Understanding: Unlocking Causal Attention into Modality-Mutual Attention for Multimodal LLMs

ORCA: An Agentic Reasoning Framework for Hallucination and Adversarial Robustness in Vision-Language Models

Receipt and verification

First computed	2026-05-18T04:33:37.170404Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

60b0d37785633fc0ac12675ccac1461d5c6257f36b2a5a10468ee6d6560931d5

Aliases

arxiv: 2402.00253 · arxiv_version: 2402.00253v2 · doi: 10.48550/arxiv.2402.00253 · pith_short_12: MCYNG54FMM74 · pith_short_16: MCYNG54FMM74BLAS · pith_short_8: MCYNG54F

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curl -sH 'Accept: application/ld+json' https://pith.science/pith/MCYNG54FMM74BLASM5OMVQKGDV \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 60b0d37785633fc0ac12675ccac1461d5c6257f36b2a5a10468ee6d6560931d5

Canonical record JSON

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