mlsanityData to 7 Oct 2026

Evaluation and Analysis of Hallucination in Large Vision-Language Models

arXiv 2308.15126 ↗First posted 29 Aug 2023cs.LG cs.AI cs.CLcited by 99

On the map: Multimodal LLMs / vision-language models → Hallucination mitigation in LVLMs

Abstract (arXiv)

Large Vision-Language Models (LVLMs) have recently achieved remarkable success. However, LVLMs are still plagued by the hallucination problem, which limits the practicality in many scenarios. Hallucination refers to the information of LVLMs' responses that does not exist in the visual input, which poses potential risks of substantial consequences. There has been limited work studying hallucination evaluation in LVLMs. In this paper, we propose Hallucination Evaluation based on Large Language Models (HaELM), an LLM-based hallucination evaluation framework. HaELM achieves an approximate 95% performance comparable to ChatGPT and has additional advantages including low cost, reproducibility, privacy preservation and local deployment. Leveraging the HaELM, we evaluate the hallucination in current LVLMs. Furthermore, we analyze the factors contributing to hallucination in LVLMs and offer helpful suggestions to mitigate the hallucination problem. Our training data and human annotation hallucination data will be made public soon.

Key facts. Cited by 99 papers in the map (data through 7 Oct 2026). Placed in Hallucination mitigation in LVLMs, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 27 of its 35 references that are in the map. First posted on arXiv 29 Aug 2023.

Builds on

27 of 35 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Evaluating Object Hallucination in Large Vision-Language Models17 May

Other references · 26

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  3. 2022Training language models to follow instructions with human feedbackcited by 15,712
  4. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  5. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  6. 2023Visual Instruction Tuningcited by 7,792
  7. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  8. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,464
  9. 2023MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Modelscited by 2,689
  10. 2022BLOOM: A 176B-Parameter Open-Access Multilingual Language Modelcited by 1,968
  11. 2015Microsoft COCO Captions: Data Collection and Evaluation Servercited by 1,447
  12. 2023HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Facecited by 1,173
14 more
  1. 2023mPLUG-Owl: Modularization Empowers Large Language Models with Multimodalitycited by 1,038
  2. 2014Deep Visual-Semantic Alignments for Generating Image Descriptionscited by 966
  3. 2023Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedbackcited by 788
  4. 2023Otter: A Multi-Modal Model with In-Context Instruction Tuningcited by 548
  5. 2023MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Actioncited by 511
  6. 2023VisionLLM: Large Language Model is also an Open-Ended Decoder for Vision-Centric Taskscited by 499
  7. 2023PandaGPT: One Model To Instruction-Follow Them Allcited by 330
  8. 2021How Much Can CLIP Benefit Vision-and-Language Tasks?cited by 321
  9. 2023How Language Model Hallucinations Can Snowballcited by 310
  10. 2023EmbodiedGPT: Vision-Language Pre-Training via Embodied Chain of Thoughtcited by 276
  11. 2023MultiModal-GPT: A Vision and Language Model for Dialogue with Humanscited by 255
  12. 2023M3IT: A Large-Scale Dataset towards Multi-Modal Multilingual Instruction Tuningcited by 126
  13. 2020Visual News: Benchmark and Challenges in News Image Captioningcited by 121
  14. 2021Let there be a clock on the beach: Reducing Object Hallucination in Image Captioningcited by 74

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 9; 2024: 49; 2025: 29; 2026: 12.

99 papers.

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  1. 2024Hallucination of Multimodal Large Language Models: A Surveycited by 380
  2. 2024MLLM-as-a-Judge: Assessing Multimodal LLM-as-a-Judge with Vision-Language Benchmarkcited by 362
  3. 2024A Survey on Hallucination in Large Vision-Language Modelscited by 352
  4. 2023Analyzing and Mitigating Object Hallucination in Large Vision-Language Modelscited by 314
  5. 2023Multimodal Foundation Models: From Specialists to General-Purpose Assistantscited by 247
  6. 2023Woodpecker: Hallucination Correction for Multimodal Large Language Modelscited by 223
  7. 2023Beyond Hallucinations: Enhancing LVLMs through Hallucination-Aware Direct Preference Optimizationcited by 223
  8. 2024Mobile-Agent-v2: Mobile Device Operation Assistant with Effective Navigation via Multi-Agent Collaborationcited by 187
  9. 2023Holistic Analysis of Hallucination in GPT-4V(ision): Bias and Interference Challengescited by 131
  10. 2024IBD: Alleviating Hallucinations in Large Vision-Language Models via Image-Biased Decodingcited by 118
  11. 2024VideoHallucer: Evaluating Intrinsic and Extrinsic Hallucinations in Large Video-Language Modelscited by 88
  12. 2023Cognitive Mirage: A Review of Hallucinations in Large Language Modelscited by 83
38 more
  1. 2024A Survey on Benchmarks of Multimodal Large Language Modelscited by 81
  2. 2024MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMscited by 72
  3. 2024MJ-Bench: Is Your Multimodal Reward Model Really a Good Judge for Text-to-Image Generation?cited by 64
  4. 2024Navigating the Risks: A Survey of Security, Privacy, and Ethics Threats in LLM-Based Agentscited by 63
  5. 2024Detecting and Evaluating Medical Hallucinations in Large Vision Language Modelscited by 57
  6. 2024Reducing Hallucinations in Vision-Language Models via Latent Space Steeringcited by 56
  7. 2024TinyChart: Efficient Chart Understanding with Visual Token Merging and Program-of-Thoughts Learningcited by 51
  8. 2024AVHBench: A Cross-Modal Hallucination Benchmark for Audio-Visual Large Language Modelscited by 50
  9. 2024CLIP-DPO: Vision-Language Models as a Source of Preference for Fixing Hallucinations in LVLMscited by 49
  10. 2024The Curse of Multi-Modalities: Evaluating Hallucinations of Large Multimodal Models across Language, Visual, and Audiocited by 48
  11. 2025DyFo: A Training-Free Dynamic Focus Visual Search for Enhancing LMMs in Fine-Grained Visual Understandingcited by 47
  12. 2024A Survey on Evaluation of Multimodal Large Language Modelscited by 46
  13. 2024Mitigating Object Hallucination in Large Vision-Language Models via Image-Grounded Guidancecited by 44
  14. 2024PathMMU: A Massive Multimodal Expert-Level Benchmark for Understanding and Reasoning in Pathologycited by 43
  15. 2024THRONE: An Object-based Hallucination Benchmark for the Free-form Generations of Large Vision-Language Modelscited by 36
  16. 2024Alleviating Hallucinations in Large Vision-Language Models through Hallucination-Induced Optimizationcited by 34
  17. 2023HallE-Control: Controlling Object Hallucination in Large Multimodal Modelscited by 31
  18. 2024Understanding Sounds, Missing the Questions: The Challenge of Object Hallucination in Large Audio-Language Modelscited by 30
  19. 2024HaloQuest: A Visual Hallucination Dataset for Advancing Multimodal Reasoningcited by 30
  20. 2024Do Vision-Language Models Really Understand Visual Language?cited by 29
  21. 2024Can Large Audio-Language Models Truly Hear? Tackling Hallucinations with Multi-Task Assessment and Stepwise Audio Reasoningcited by 29
  22. 2024Evaluating and Analyzing Relationship Hallucinations in Large Vision-Language Modelscited by 27
  23. 2024A Survey on Multimodal Benchmarks: In the Era of Large AI Modelscited by 27
  24. 2025A Survey of Multimodal Hallucination Evaluation and Detectioncited by 26
  25. 2024LVLM-Interpret: An Interpretability Tool for Large Vision-Language Modelscited by 23
  26. 2024Deconstructing The Ethics of Large Language Models from Long-standing Issues to New-emerging Dilemmas: A Surveycited by 23
  27. 2024Benchmarking Vision, Language, & Action Models on Robotic Learning Taskscited by 23
  28. 2024Implicit Multimodal Alignment: On the Generalization of Frozen LLMs to Multimodal Inputscited by 22
  29. 2024BEAF: Observing BEfore-AFter Changes to Evaluate Hallucination in Vision-language Modelscited by 18
  30. 2024Long-Form Answers to Visual Questions from Blind and Low Vision Peoplecited by 18
  31. 2024Sora Detector: A Unified Hallucination Detection for Large Text-to-Video Modelscited by 17
  32. 2024Fine-Grained Verifiers: Preference Modeling as Next-token Prediction in Vision-Language Alignmentcited by 17
  33. 2025VASparse: Towards Efficient Visual Hallucination Mitigation via Visual-Aware Token Sparsificationcited by 17
  34. 2024Pensieve: Retrospect-then-Compare Mitigates Visual Hallucinationcited by 14
  35. 2024MetaToken: Detecting Hallucination in Image Descriptions by Meta Classificationcited by 13
  36. 2024Certainly Uncertain: A Benchmark and Metric for Multimodal Epistemic and Aleatoric Awarenesscited by 13
  37. 2024ESREAL: Exploiting Semantic Reconstruction to Mitigate Hallucinations in Vision-Language Modelscited by 12
  38. 2025Aligning Multimodal LLM with Human Preference: A Surveycited by 12

50 most cited of 99 All 99 (JSON)

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  1. 2024Measuring the Measurers: Quality Evaluation of Hallucination Benchmarks for Large Vision-Language Models
  2. 2024Hallucination in Multimodal Foundation Models: A Survey on Causes, Corrections, and Evaluations
  3. 2024VALOR-EVAL: Holistic Coverage and Faithfulness Evaluation of Large Vision-Language Models
  4. 2023HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models
  5. 2024Hal-Eval: A Universal and Fine-grained Hallucination Evaluation Framework for Large Vision Language Models
  6. 2025A Comprehensive Analysis for Visual Object Hallucination in Large Vision-Language Models
  7. 2024LongHalQA: Long-Context Hallucination Evaluation for MultiModal Large Language Models
  8. 2024Detecting and Mitigating Hallucination in Large Vision Language Models via Fine-Grained AI Feedback