mlsanityData to 7 Oct 2026

Logical Closed Loop: Uncovering Object Hallucinations in Large Vision-Language Models

arXiv 2402.11622 ↗First posted 18 Feb 2024cs.CV cs.AI cs.CLcited by 47

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

Abstract (arXiv)

Object hallucination has been an Achilles' heel which hinders the broader applications of large vision-language models (LVLMs). Object hallucination refers to the phenomenon that the LVLMs claim non-existent objects in the image. To mitigate the object hallucinations, instruction tuning and external model-based detection methods have been proposed, which either require large-scare computational resources or depend on the detection result of external models. However, there remains an under-explored field to utilize the LVLM itself to alleviate object hallucinations. In this work, we adopt the intuition that the LVLM tends to respond logically consistently for existent objects but inconsistently for hallucinated objects. Therefore, we propose a Logical Closed Loop-based framework for Object Hallucination Detection and Mitigation, namely LogicCheckGPT. In specific, we devise logical consistency probing to raise questions with logical correlations, inquiring about attributes from objects and vice versa. Whether their responses can form a logical closed loop serves as an indicator of object hallucination. As a plug-and-play method, it can be seamlessly applied to all existing LVLMs. Comprehensive experiments conducted on three benchmarks across four LVLMs have demonstrated significant improvements brought by our method, indicating its effectiveness and generality.

Key facts. Cited by 47 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 16 of its 30 references that are in the map. First posted on arXiv 18 Feb 2024.

Builds on

16 of 30 references.

Closest ancestors · 5

Same direction, newest first

  1. 2024Unified Hallucination Detection for Multimodal Large Language Models5 Feb
  2. 2023OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocation29 Nov
  3. 2023Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding28 Nov
  4. 2023Volcano: Mitigating Multimodal Hallucination through Self-Feedback Guided Revision13 Nov
  5. 2023Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning26 Jun

Other references · 11

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2022Training language models to follow instructions with human feedbackcited by 15,712
  4. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  5. 2023InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuningcited by 2,855
  6. 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,201
  7. 2023MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Modelscited by 1,624
  8. 2023mPLUG-Owl: Modularization Empowers Large Language Models with Multimodalitycited by 1,038
  9. 2023Otter: A Multi-Modal Model with In-Context Instruction Tuningcited by 548
  10. 2023LM vs LM: Detecting Factual Errors via Cross Examinationcited by 155
  11. 2023VIGC: Visual Instruction Generation and Correctioncited by 80

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 16; 2025: 17; 2026: 14.

47 papers.

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  1. 2024Devils in Middle Layers of Large Vision-Language Models: Interpreting, Detecting and Mitigating Object Hallucinations via Attention Lenscited by 139
  2. 2024RLAIF-V: Open-Source AI Feedback Leads to Super GPT-4V Trustworthinesscited by 129
  3. 2024Self-Introspective Decoding: Alleviating Hallucinations for Large Vision-Language Modelscited by 90
  4. 2024Mitigating Object Hallucinations in Large Vision-Language Models with Assembly of Global and Local Attentioncited by 74
  5. 2024Mitigating Object Hallucination via Concentric Causal Attentioncited by 48
  6. 2024Don't Miss the Forest for the Trees: Attentional Vision Calibration for Large Vision Language Modelscited by 46
  7. 2024Nullu: Mitigating Object Hallucinations in Large Vision-Language Models via HalluSpace Projectioncited by 46
  8. 2025Seeing Far and Clearly: Mitigating Hallucinations in MLLMs with Attention Causal Decodingcited by 42
  9. 2024ICT: Image-Object Cross-Level Trusted Intervention for Mitigating Object Hallucination in Large Vision-Language Modelscited by 28
  10. 2025Cross-Modal Attention Calibration for LVLM Hallucination Mitigationcited by 21
  11. 2025Exploring Hallucination of Large Multimodal Models in Video Understanding: Benchmark, Analysis and Mitigationcited by 20
  12. 2024MetaToken: Detecting Hallucination in Image Descriptions by Meta Classificationcited by 13
35 more
  1. 2024VaLiD: Mitigating the Hallucination of Large Vision Language Models by Visual Layer Fusion Contrastive Decodingcited by 12
  2. 2025Do You Keep an Eye on What I Ask? Mitigating Multimodal Hallucination via Attention-Guided Ensemble Decodingcited by 12
  3. 2025TruthPrInt: Mitigating Large Vision-Language Models Object Hallucination Via Latent Truthful-Guided Pre-Interventioncited by 11
  4. 2025OmniDPO: A Preference Optimization Framework to Address Omni-Modal Hallucinationcited by 8
  5. 2024Unified Triplet-Level Hallucination Evaluation for Large Vision-Language Modelscited by 7
  6. 2024RAC3: Retrieval-Augmented Corner Case Comprehension for Autonomous Driving with Vision-Language Modelscited by 7
  7. 2026Look Carefully: Adaptive Visual Reinforcements in Multimodal Large Language Models for Hallucination Mitigationcited by 7
  8. 2024Prescribing the Right Remedy: Mitigating Hallucinations in Large Vision-Language Models via Targeted Instruction Tuningcited by 6
  9. 2025VisioMath: Benchmarking Figure-based Mathematical Reasoning in LMMscited by 6
  10. 2025Intervene-All-Paths: Unified Mitigation of LVLM Hallucinations across Alignment Formatscited by 6
  11. 2025Med-VCD: Mitigating Hallucination for Medical Large Vision Language Models through Visual Contrastive Decodingcited by 6
  12. 2025UniVRSE: Unified Vision-conditioned Response Semantic Entropy for Hallucination Detection in Medical Vision-Language Modelscited by 5
  13. 2024Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysiscited by 4
  14. 2024Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarioscited by 4
  15. 2026FREAK: A Fine-grained Hallucination Evaluation Benchmark for Advanced MLLMscited by 4
  16. 2024LLMs Can Check Their Own Results to Mitigate Hallucinations in Traffic Understanding Taskscited by 3
  17. 2026CRoPS: A Training-Free Hallucination Mitigation Framework for Vision-Language Modelscited by 3
  18. 2026Beyond the Global Scores: Fine-Grained Token Grounding as a Robust Detector of LVLM Hallucinationscited by 3
  19. 2025TPC: Cross-Temporal Prediction Connection for Vision-Language Model Hallucination Reductioncited by 2
  20. 2025Hydra: An Agentic Reasoning Approach for Enhancing Adversarial Robustness and Mitigating Hallucinations in Vision-Language Modelscited by 2
  21. 2025Empowering Multimodal LLMs with External Tools: A Comprehensive Surveycited by 2
  22. 2026V-Loop: Visual Logical Loop Verification for Hallucination Detection in Medical Visual Question Answeringcited by 2
  23. 2026Correcting Visual Blur Induced by Attention Distraction to Reduce Hallucinations: Algorithm and Theorycited by 2
  24. 2025Efficient Contrastive Decoding with Probabilistic Hallucination Detection - Mitigating Hallucinations in Large Vision Language Models -cited by 1
  25. 2026AD-MIR: Bridging the Gap from Perception to Persuasion in Advertising Video Understanding via Structured Reasoningcited by 1
  26. 2026Mitigating Object Hallucinations in LVLMs via Attention Imbalance Rectificationcited by 1
  27. 2025Curing Semantic Drift: A Dynamic Approach to Grounding Generation in Large Vision-Language Modelscited by 0
  28. 2025ORCA: An Agentic Reasoning Framework for Hallucination and Adversarial Robustness in Vision-Language Modelscited by 0
  29. 2025Visual Reasoning Agent: Robust Vision Systems in Remote Sensing via Inference-Time Scalingcited by 0
  30. 2026R-CoV: Region-Aware Chain-of-Verification for Alleviating Object Hallucinations in LVLMscited by 0
  31. 2026Through the Lens of Character: Resolving Modality-Role Interference in Multimodal Role-Playing Agentcited by 0
  32. 2026VIHD: Visual Intervention-based Hallucination Detection for Medical Visual Question Answeringcited by 0
  33. 2026Test-Time Hallucination Control in Large Vision-Language Modelscited by 0
  34. 2026Dual-Stream Cross-Anchor Correction Grounding Long-Form Captions and the Domain Limits of Object-Level Anchorscited by 0
  35. 2026Semantic-Spatial Agreement Verification for Mitigating Object Hallucination in Multimodal Large Language Modelscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Evaluating Hallucination in Large Vision-Language Models based on Context-Aware Object Similarities
  2. 2023Analyzing and Mitigating Object Hallucination in Large Vision-Language Models
  3. 2026TruthLens: Object Hallucination Detection via Self-Evaluating Truthfulness Scores in LVLMs
  4. 2023Evaluating Object Hallucination in Large Vision-Language Models
  5. 2025Causal-HalBench: Uncovering LVLMs Object Hallucinations Through Causal Intervention
  6. 2024Multi-Object Hallucination in Vision-Language Models
  7. 2026HaloProbe: Bayesian Detection and Mitigation of Object Hallucinations in Vision-Language Models
  8. 2025Analyzing and Mitigating Object Hallucination: A Training Bias Perspective