mlsanityData to 7 Oct 2026

Unified Hallucination Detection for Multimodal Large Language Models

arXiv 2402.03190 ↗First posted 5 Feb 2024cs.CL cs.AI cs.IRcited by 75

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

Abstract (arXiv)

Despite significant strides in multimodal tasks, Multimodal Large Language Models (MLLMs) are plagued by the critical issue of hallucination. The reliable detection of such hallucinations in MLLMs has, therefore, become a vital aspect of model evaluation and the safeguarding of practical application deployment. Prior research in this domain has been constrained by a narrow focus on singular tasks, an inadequate range of hallucination categories addressed, and a lack of detailed granularity. In response to these challenges, our work expands the investigative horizons of hallucination detection. We present a novel meta-evaluation benchmark, MHaluBench, meticulously crafted to facilitate the evaluation of advancements in hallucination detection methods. Additionally, we unveil a novel unified multimodal hallucination detection framework, UNIHD, which leverages a suite of auxiliary tools to validate the occurrence of hallucinations robustly. We demonstrate the effectiveness of UNIHD through meticulous evaluation and comprehensive analysis. We also provide strategic insights on the application of specific tools for addressing various categories of hallucinations.

Key facts. Cited by 75 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 33 of its 51 references that are in the map. First posted on arXiv 5 Feb 2024.

Builds on

33 of 51 references.

Closest ancestors · 4

Same direction, newest first

  1. 2023OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocation29 Nov
  2. 2023Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding28 Nov
  3. 2023AMBER: An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluation13 Nov
  4. 2023Analyzing and Mitigating Object Hallucination in Large Vision-Language Models1 Oct

Other references · 29

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  3. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
  4. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
  5. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
  6. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,064
  7. 2023Let's Verify Step by Stepcited by 3,598
  8. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,317
  9. 2023Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detectioncited by 3,274
  10. 2023MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Modelscited by 2,689
  11. 2019Towards VQA Models That Can Readcited by 1,696
  12. 2023mPLUG-Owl: Modularization Empowers Large Language Models with Multimodalitycited by 1,038
17 more
  1. 2019Evaluating the Factual Consistency of Abstractive Text Summarizationcited by 578
  2. 2020Asking and Answering Questions to Evaluate the Factual Consistency of Summariescited by 376
  3. 2023A Survey of Hallucination in Large Foundation Modelscited by 365
  4. 2024A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Modelscited by 266
  5. 2023FacTool: Factuality Detection in Generative AI -- A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarioscited by 247
  6. 2023ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddingscited by 238
  7. 2023Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificitycited by 204
  8. 2023TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIscited by 184
  9. 2024Agent AI: Surveying the Horizons of Multimodal Interactioncited by 167
  10. 2024Fine-grained Hallucination Detection and Editing for Language Modelscited by 146
  11. 2023Alleviating Hallucinations of Large Language Models through Induced Hallucinationscited by 78
  12. 2023A Survey on Detection of LLMs-Generated Contentcited by 70
  13. 2023Making Language Models Better Tool Learners with Execution Feedbackcited by 69
  14. 2023WikiChat: Stopping the Hallucination of Large Language Model Chatbots by Few-Shot Grounding on Wikipediacited by 67
  15. 2023Do Large Language Models Know about Facts?cited by 49
  16. 2023Revisiting Scene Text Recognition: A Data Perspectivecited by 45
  17. 2023FactCHD: Benchmarking Fact-Conflicting Hallucination Detectioncited by 29

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 31; 2025: 29; 2026: 15.

75 papers.

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  1. 2024VideoHallucer: Evaluating Intrinsic and Extrinsic Hallucinations in Large Video-Language Modelscited by 88
  2. 2024MLLM can see? Dynamic Correction Decoding for Hallucination Mitigationcited by 87
  3. 2024A Survey on Benchmarks of Multimodal Large Language Modelscited by 81
  4. 2024MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMscited by 72
  5. 2024VidHalluc: Evaluating Temporal Hallucinations in Multimodal Large Language Models for Video Understandingcited by 65
  6. 2024Knowledge Circuits in Pretrained Transformerscited by 60
  7. 2024Detecting and Evaluating Medical Hallucinations in Large Vision Language Modelscited by 57
  8. 2024Visual Agents as Fast and Slow Thinkerscited by 53
  9. 2024The Curse of Multi-Modalities: Evaluating Hallucinations of Large Multimodal Models across Language, Visual, and Audiocited by 48
  10. 2024Logical Closed Loop: Uncovering Object Hallucinations in Large Vision-Language Modelscited by 47
  11. 2024A Survey on Evaluation of Multimodal Large Language Modelscited by 46
  12. 2024Assessment of Multimodal Large Language Models in Alignment with Human Valuescited by 36
38 more
  1. 2024A Survey on Multimodal Benchmarks: In the Era of Large AI Modelscited by 27
  2. 2025A Survey of Multimodal Hallucination Evaluation and Detectioncited by 26
  3. 2025Granite Vision: a lightweight, open-source multimodal model for enterprise Intelligencecited by 20
  4. 2025Exploring Hallucination of Large Multimodal Models in Video Understanding: Benchmark, Analysis and Mitigationcited by 20
  5. 2024VidHal: Benchmarking Temporal Hallucinations in Vision LLMscited by 19
  6. 2024CrossCheckGPT: Universal Hallucination Ranking for Multimodal Foundation Modelscited by 18
  7. 2024Joint Visual and Text Prompting for Improved Object-Centric Perception with Multimodal Large Language Modelscited by 16
  8. 2024The Synergy between Data and Multi-Modal Large Language Models: A Survey from Co-Development Perspectivecited by 14
  9. 2024MetaToken: Detecting Hallucination in Image Descriptions by Meta Classificationcited by 13
  10. 2024Collu-Bench: A Benchmark for Predicting Language Model Hallucinations in Codecited by 13
  11. 2024Enhancing Few-Shot Vision-Language Classification with Large Multimodal Model Featurescited by 11
  12. 2025MMBoundary: Advancing MLLM Knowledge Boundary Awareness through Reasoning Step Confidence Calibrationcited by 11
  13. 2024Advancing Fine-Grained Visual Understanding with Multi-Scale Alignment in Multi-Modal Modelscited by 10
  14. 2025Through the Magnifying Glass: Adaptive Perception Magnification for Hallucination-Free VLM Decodingcited by 10
  15. 2025HalLoc: Token-level Localization of Hallucinations for Vision Language Modelscited by 9
  16. 2024Fake Artificial Intelligence Generated Contents (FAIGC): A Survey of Theories, Detection Methods, and Opportunitiescited by 7
  17. 2024Unified Triplet-Level Hallucination Evaluation for Large Vision-Language Modelscited by 7
  18. 2025Mitigating Hallucination in VideoLLMs via Temporal-Aware Activation Engineeringcited by 7
  19. 2024SELU: Self-Learning Embodied MLLMs in Unknown Environmentscited by 6
  20. 2025Loki's Dance of Illusions: A Comprehensive Survey of Hallucination in Large Language Modelscited by 6
  21. 2025UniVRSE: Unified Vision-conditioned Response Semantic Entropy for Hallucination Detection in Medical Vision-Language Modelscited by 5
  22. 2025EmotionHallucer: Evaluating Emotion Hallucinations in Multimodal Large Language Modelscited by 5
  23. 2025ZINA: Multimodal Fine-grained Hallucination Detection and Editingcited by 5
  24. 2025HalDec-Bench: Benchmarking Hallucination Detector in Image Captioningcited by 5
  25. 2024Evaluating and Enhancing Trustworthiness of LLMs in Perception Taskscited by 4
  26. 2024Pre-Training Multimodal Hallucination Detectors with Corrupted Grounding Datacited by 4
  27. 2025CutPaste&Find: Efficient Multimodal Hallucination Detector with Visual-aid Knowledge Basecited by 4
  28. 2025Uncertainty-o: One Model-agnostic Framework for Unveiling Uncertainty in Large Multimodal Modelscited by 4
  29. 2025Bridging Vision Language Models and Symbolic Grounding for Video Question Answeringcited by 4
  30. 2025HalluShift++: Bridging Language and Vision through Internal Representation Shifts for Hierarchical Hallucinations in MLLMscited by 4
  31. 2026FREAK: A Fine-grained Hallucination Evaluation Benchmark for Advanced MLLMscited by 4
  32. 2026SpatialReward: Verifiable Spatial Reward Modeling for Fine-Grained Spatial Consistency in Text-to-Image Generationcited by 4
  33. 2024LLMs Can Check Their Own Results to Mitigate Hallucinations in Traffic Understanding Taskscited by 3
  34. 2024FIHA: Autonomous Hallucination Evaluation in Vision-Language Models with Davidson Scene Graphscited by 3
  35. 2025Grounding or Guessing? Visual Signals for Detecting Hallucinations in Sign Language Translationcited by 3
  36. 2026Halluverse-M^3: A multitask multilingual benchmark for hallucination in LLMscited by 3
  37. 2026MoD-DPO: Towards Mitigating Cross-modal Hallucinations in Omni LLMs using Modality Decoupled Preference Optimizationcited by 3
  38. 2025SNS-Bench-VL: Benchmarking Multimodal Large Language Models in Social Networking Servicescited by 2

50 most cited of 75 All 75 (JSON)

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  1. 2026HalluScope: Fine-grained Hallucination Diagnosis for Multimodal Large Language Models
  2. 2025HalluSearch at SemEval-2025 Task 3: A Search-Enhanced RAG Pipeline for Hallucination Detection
  3. 2026OmniHallu: Unified Hallucination Detection for Cross-Modal Comprehension and Generation in Multimodal Large Language Models
  4. 2024Hallucination of Multimodal Large Language Models: A Survey
  5. 2025(Im)possibility of Automated Hallucination Detection in Large Language Models
  6. 2024Hallucination in Multimodal Foundation Models: A Survey on Causes, Corrections, and Evaluations
  7. 2025CCHall: A Novel Benchmark for Joint Cross-Lingual and Cross-Modal Hallucinations Detection in Large Language Models
  8. 2025Mitigating Behavioral Hallucination in Multimodal Large Language Models for Sequential Images