Awesome-LVLM-Hallucination

Even though the world has seen the imersive capabilities of large vision language models, particularly in zero-shot inference, such models struggle with hallucinations, which can be referred to as the generation of text with information that is not present in the visual input. Lots of research work is going on to tackle this problem, such as hallucinated objects, inaccurate attributes and relationships, unfaithful descriptions, and so on. Possible reasons behind this could be language prior, insufficient visual context, biases and misinformation in the training dataset, and lot more.

This repository will provide an organized list of state-of-the-art research papers, relevant code, and a brief description related to hallucinations of the Large-Vision-Language Model (LVLM), also known as the Multimodal Large Language Model (MLLM).

The main intention of this project is to provide a platform where all the research work in the field of hallucination in LVLMs is accessed in a constructive way. If you have any suggestions for intersecting work within this field, kindly contribute them by raising an open issue. I am looking forward to fruitful discussion and learning!

---

• Awesome LVLM Hallucination
  • Hallucination Evaluation Benchmark
  • Hallucination Detection
  • Hallucination Mitigation
  • Survey

---

Evaluation-Benchmark

1. CHAIR: Object Hallucination in Image Captioning (EMNLP 2018)

   • Introduce problem of object hallucination on MSCOCO image captioning task
   • CHAIR metrics [built upon unique 80 MSCOCO dataset objects]

2. POPE: Evaluating Object Hallucination in Large Vision-Language Models (EMNLP 2023)

   • Object existence hallucination [Yes/No]
   • Random, Popular and Adversial settings on MSCOCO dataset

3. MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models (23 June, 2023)

   • MME benchmark covers the evaluation of MLLM's perception and cognition abilities
   • Perception (Coase-Grained): 4; Perception (Fine-Grained): 5; Perception (OCR): 1; Cognition (Reasoning): 4; [Total 14 subtasks]
   • Answer in Yes/No format for easy evaluation & 30 advanced MLLMs are benchmarked

4. M-HalDetect: Detecting and Preventing Hallucinations in Large Vision Language Models (AAAI 2024)

   • Hallucination detection dataset with fine-grained annotations [accurate, inaccurate and analysis]
   • Fine-grained Direct Preference Optimization (FDPO) technique and reward model dataset
   • High correlation of reward model score with human evaluation

5. HaELM: Evaluation and Analysis of Hallucination in Large Vision-Language Models (29 August, 2023)

   • Discussed LVLMs tendency to response as 'Yes' to judgement type queries
   • Use of ChatGPT to collect hallucination data via iterative prompt modification
   • Open-source LLM trained over this dataset for evaluation of LVLM's response
   • Evaluation results on various LVLMs, Generation length and Top-K of sampling

6. CIEM: Contrastive Instruction Evaluation Method for Better Instruction Tuning (NeurIPS 2023 Workshop)

   • Automatic construction of question-answer pair with based on dataset with caption annotation using ChatGPT [Yes/No QA pair] and automatic pipeline for evaluation
   • Constractive instruction tuning (CIT) with Factual and Constractive QA pairs with Chain-of-Thought (CoT) justification

7. CAST: Cross-modal Alignment Similarity Test for Vision Language Models (17 September, 2024)

   • Proposed CAST as a way to measure the self-consistency of LVLMs across different modalities.
   • This works in two stage, in the first stage the models generate similarities/true statements comparing two inputs, and in the second stage the model judges its own output for truthfulness.

8. MMHAL-BENCH: Aligning Large Multimodal Models with Factually Augmented RLHF (25 September, 2023)

   • Introduced novel algorithm called Factually Augmented RLHF (Fact-RLHF) to alleviate the reward hacking phenomenon in RLHF
   • Developed evaluation benchmark MMHAL-BENCH with a special focus on penalizing hallucinations
   • Trained a LLM with RLHF (Llava-RLHF) which shows improved multimodal alignment

9. LRV (GAVIE): Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning (29 September, 2023)

   • LRV-Instruction - positive and negative robust instruction tuning dataset with 400k visual instructions (16 tasks)
   • Negative instruction semantics: (a) Nonexistent Object Manipulation (b) Existent Object Manipulation (c) Knowledge Manipulation
   • GPT4-Assisted Visual Instruction Evaluation (GAVIE)

10. NOPE: Negative Object Presence Evaluation (NOPE) to Measure Object Hallucination in Vision-Language Models (09 October, 2023)

    • VQA diagnostic benchmark to measure object hallucination with use of 'Negative Pompt' based questions
    • LLM based generation of 29.5k synthetic negative pronoum (none, no one, nobody. nowhere, neither) dataset
    • Finding: tendency of VLMs to hallucinate more on data with higher lexical diversity, more scene relavent objects (co-occurance) and large answer copes.

11. HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models (CVPR 2024)

    • Language Hallucination + Visual Illusion: 1129 VQA paired with total 346 images
    • It includes topics such as food, math, geometry, statistics, geography, sports, cartoon, famous illusions, movie, meme, etc. and formats such as including logo, poster, figure, charts, table, map, consecutive images, etc.

12. FAITHSCORE: Evaluating Hallucinations in Large Vision-Language Models (02 November, 2023)

    • Reference-free and fine-grained evaluation metric
    • 1. Recognizer : LLM is used for descriptive content identification of LVLM's prediction
    • 2. Decomposer : LLM is used to generate atomic facts based on recognizer's output
    • 3. Verifier : Visual Entailment Model (e.g. OFA) is used to verify atomic facts with input image

13. Bingo: Holistic Analysis of Hallucination in GPT-4V(ision): Bias and Interference Challenges (07 November, 2023)

    • Total 308 Images and 370 QA Pairs
    • Bias category: Region, OCR and Factual
    • Interferance catogary: Image-to-Image and Text-to-Image

14. AMBER: An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluation (13 November, 2023)

    • LLM free evaluation of hallucination using AMBER benchmark
    • Evaluation of hallucination for generative and discriminative task using AMBERSCORE metric (covers existence, attributes and relation types of hallucination)
    • Includes hallucinatory target objects (more likely to be imagined by LVLMs)

15. RAH-Bench: Mitigating Hallucination in Visual Language Models with Visual Supervision (27 Novemebr, 2023)

    • Introduce fine-grained vision instruction dataset named RAI-30K (built upon panoptic scene graph dataset (PSG))
    • RAH-BENCH vision hallucination evaluation benchmark (3 types: Categorial, Relation and Attribute Hallucination)
    • False Positive Rates as evaluation metric

16. Behind the Magic, MERLIM: Multi-modal Evaluation Benchmark for Large Image-Language Models (03 Decemeber, 2023)

    • Proposed a novel test-bed to evaluate IT-LVLMs (Instruction Tuning Large Vision and Language models) on core computer vision tasks
    • Observed poor performance of IT-LVLMs with multiple failure cases in visual grounding
    • Identify problems with IT-LVLMSs like generation of hallucinatory events and sensitivity to the input query

17. CCEval: HallE-Switch: Controlling Object Hallucination in Large Vision Language Models (03 Decemebr, 2023)

    • Suggest an approach to control object existence hallucination in detailed captions of LVLM
    • Introduced CCEval which is a GPT-4 assisted evaluation method for detailed captioning (Metrics: CHAIR(i&s), Coverage, Average Length, Average Objects)
    • Detailed investigation on LVLM's component that might imfluence hallucination such as alignment of language decoder, volume of instruction data, resolution of input image and so on
    • Introduced a controlling parameters over LLMs (HallE-Control) to condition the inference of objects

18. FGHE: Mitigating Fine-Grained Hallucination by Fine-Tuning Large Vision-Language Models with Caption Rewrites (04 December, 2023)

    • Dealing with fine-grained object hallucination with ReCaption framework
    • Two stage frame work : 1) Caption generation with help of ChatGPT 2) Finetuning LVLMs on generated captions
    • Inroduced Fine-Grained Object Hallucination Evaluation (FGHE) which similar to POPE. (manually annotted 50 images with 200 binary questions with type multi-object, attributes and behaviour)

19. OpenCHAIR: Mitigating Open-Vocabulary Caption Hallucinations (06 Decemeber, 2023)

    • soon

20. CorrelationQA: The Instinctive Bias: Spurious Images lead to Hallucination in MLLMs (06 February, 2024)

    • soon

21. ViGoR: Improving Visual Grounding of Large Vision Language Models with Fine-Grained Reward Modeling (09 February, 2024)

    • soon

22. VQAv2-IDK: Visually Dehallucinative Instruction Generation: Know What You Don’t Know (15 February, 2024)

    • soon

23. MHaluBench: Unified Hallucination Detection for Multimodal Large Language Models (20 February, 2024)

    • soon

24. MAD-Bench: How Easy is It to Fool Your Multimodal LLMs? An Empirical Analysis on Deceptive Prompts (20 February, 2024)

    • soon

25. VHTest: Visual Hallucinations of Multi-modal Large Language Models (22 February, 2024)

    • soon

26. Hal-Eval: A Universal and Fine-grained Hallucination Evaluation Framework for Large Vision Language Models (24 February, 2024)

    • soon

27. Evaluating and Mitigating Number Hallucinations in Large Vision-Language Models: A Consistency Perspective (03 March, 2024)

    • soon

28. ** EvalDial**: Mitigating Dialogue Hallucination for Large Multi-modal Models via Adversarial Instruction Tuning (15 March, 2024)

    • soon

29. IVL-Hallu: PhD: A Prompted Visual Hallucination Evaluation Dataset (17 March, 2024)

    • soon

30. Unsolvable Problem Detection: Evaluating Trustworthiness of Vision Language Models (29 March, 2024)

    • soon

31. ALOHa: A New Measure for Hallucination in Captioning Models (3 April, 2024)

    • soon

32. VALOR-EVAL: Holistic Coverage and Faithfulness Evaluation of Large Vision-Language Models (22 April, 2024)

    • soon

33. THRONE: An Object-based Hallucination Benchmark for the Free-form Generations of Large Vision-Language Models (08 May, 2024)

    • soon

34. MRHal-Bench: Automated Multi-level Preference for MLLMs (18 May, 2024)

    • soon

35. VLind-Bench: Measuring Language Priors in Large Vision-Language Models (13 June, 2024)

    • soon

36. MMRel: A Relation Understanding Dataset and Benchmark in the MLLM Era (13 June, 2024)

    • soon

37. Med-HallMark: Detecting and Evaluating Medical Hallucinations in Large Vision Language Models (14 June, 2024)

    • Medical field hallucination benchmark
    • MediHall Score - evaluation metric

38. AUTOHALLUSION: Automatic Generation of Hallucination Benchmarks for Vision-Language Models (16 June, 2024)

    • soon

39. MFC-Bench: Benchmarking Multimodal Fact-Checking with Large Vision-Language Models (17 June, 2024)

    • soon

40. CHAIR-MEN: Does Object Grounding Really Reduce Hallucination of Large Vision-Language Models? (20 June, 2024)

    • soon

41. R-BENCH: Evaluating and Analyzing Relationship Hallucinations in Large Vision-Language Models (24 June, 2024) (ICML2024)

    • Introduce an evaluation benchmark to tackle relation type of hallucination
    • soon

42. HQH: Evaluating the Quality of Hallucination Benchmarks for Large Vision-Language Models (24 June, 2024)

    • Propose a framework called Hallucination benchmark Quality Measurement (HQM) to assess the quality of existing hallucination benchmarks
    • soon

43. VideoHallucer: Evaluating Intrinsic and Extrinsic Hallucinations in Large Video-Language Models (24 June, 2024)

    • soon

44. MMHalSnowball: Investigating and Mitigating the Multimodal Hallucination Snowballing in Large Vision-Language Models (30 June, 2024)

    • soon

45. MedVH: Towards Systematic Evaluation of Hallucination for Large Vision Language Models in the Medical Context (03 July, 2024)

    • soon

46. ROPE: Multi-Object Hallucination in Vision-Language Models (08 July, 2024)

    • Deals with multi-object hallucinations and their cause
    • Introduce Recognition-based Object Probing Evaluation (ROPE) for assessing multi-object hallucination
    • In-depth analysis of hallucinatory behaviors

47. BEAF: Observing BEfore-AFter Changes to Evaluate Hallucination in Vision-language Models (18 July, 2024) (ECCV 2024)

    • Proposed a hallucination evaluation benchmark called BEfore-After (BEAF)
    • New metrics introduced: True Understanding (TU), IGnorance (IG), StuBbornness (SB), and InDecision (ID)

48. HaloQuest: A Visual Hallucination Dataset for Advancing Multimodal Reasoning (22 July, 2024) (ECCV 2024)

    • Introduced a novel VQA dataset for VLM evaluation
    • soon

49. MMINSTRUCT: A High-Quality Multi-Modal Instruction Tuning Dataset with Extensive Diversity (22 July, 2024)

    • Introduced high-quality and diverse visual instruction tuning dataset
    • Claims SOTA performance of MMINSTRUCT finetuned LLava-1.5 on 10 out of 12 famous benchmarks

50. Hallu-PI: Evaluating Hallucination in Multi-modal Large Language Models within Perturbed Inputs (02 August, 2024)

    • Constructed hallucination evaluation benchmark with perturbed inputs with 7 different purturbed scenarios
    • 12 SOTA MLLMs are benchmarked

51. Reefknot: A Comprehensive Benchmark for Relation Hallucination Evaluation, Analysis and Mitigation in Multimodal Large Language Models (18th August, 2024)

    • Introduced a benchmark to evaluate relation hallucination which further catogarized in to Perceptive and Cognitice type
    • 3 evaluation tasks: Yes/No, MCQ, VQA
    • code and dataset will be released after paper's acceptance

52. Pfram: Understanding Multimodal Hallucination with Parameter-Free Representation Alignment (02 September, 2024)

    • soon

53. ODE: Open-Set Evaluation of Hallucinations in Multimodal Large Language Models (14 September, 2024)

    • soon

54. LLSAVisionQA: Explore the Hallucination on Low-level Perception for MLLMs (15 September, 2024)

    • soon

55. CAST: Cross-modal Alignment Similarity Test for Vision Language Models (17 September, 2024)

    • soon

56. JourneyBench: Challenging One-Stop Vision-Language Understanding Benchmark of Generated Images (25 September, 2024)

    • soon

57. FIHA: Autonomous Hallucination Evaluation in Vision-Language Models with Davidson Scene Graphs (20 September, 2024)

    • code: here
    • soon

58. EventHallusion: Diagnosing Event Hallucinations in Video LLMs (25 September, 2024)

    • soon

59. TUBench: Benchmarking Large Vision-Language Models on Trustworthiness with Unanswerable Questions (05 October, 2024)

    • soon

60. LongHalQA: Long-Context Hallucination Evaluation for MultiModal Large Language Models (15 October, 2024)

    • soon

61. MM-SY: Have the VLMs Lost Confidence? A Study of Sycophancy in VLMs (15 October, 2024)

    • soon (code and benchmark)

62. Magnifier Prompt: Tackling Multimodal Hallucination via Extremely Simple Instructions (15 October, 2024)

    • soon

63. DeCo: MLLM can see? Dynamic Correction Decoding for Hallucination Mitigation (15 October, 2024)

    • decoding technique
    • soon

64. The Curse of Multi-Modalities: Evaluating Hallucinations of Large Multimodal Models across Language, Visual, and Audio (16 October, 2024)

    • soon

65. Trust but Verify: Programmatic VLM Evaluation in the Wild (17 October, 2024)

    • project_page
    • soon

66. Tri-HE: Unified Triplet-Level Hallucination Evaluation for Large Vision-Language Models (03 November, 2024)

    • soon

67. H-POPE: Hierarchical Polling-based Probing Evaluation of Hallucinations in Large Vision-Language Models (06 November, 2024)

    • soon

68. VIDHAL: Benchmarking Temporal Hallucinations in Vision LLMs (25 November 2024)

    • perfromance evaluation on video / frames
    • soon

69. HALLUCINOGEN: A Benchmark for Evaluating Object Hallucination in Large Visual-Language Models (29 December, 2024)

    • soon

70. CAOS: Evaluating Hallucination in Large Vision-Language Models based on Context-Aware Object Similarities (25 January, 2024)

    • soon

71. Mirage in the Eyes: Hallucination Attack on Multi-modal Large Language Models with Only Attention Sink (25 January, 2025)

    • HF link
    • soon

72. LanP: Rethinking the Impact of Language Priors in Large Vision-Language Models (17 February, 2025)

    • soon

73. MedHallTune: An Instruction-Tuning Benchmark for Mitigating Medical Hallucination in Vision-Language Models (28 February, 2025)

    • soon

74. RePOPE: Impact of Annotation Errors on the POPE Benchmark (22 April, 2025)

    • soon

75. Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception (07 May, 2025) (CVPR 2025)

    • soon

76. QAVisualGenome & QA-FB15k: A Comprehensive Analysis for Visual Object Hallucination in Large Vision-Language Models (04 May, 2025)

    • soon

77. Localizing Before Answering: A Hallucination Evaluation Benchmark for Grounded Medical Multimodal LLMs (05 May, 2025)

    • soon

78. EmotionHallucer: Evaluating Emotion Hallucinations in Multimodal Large Language Models (16 May, 2025)

    • soon

79. MIRAGE: Assessing Hallucination in Multimodal Reasoning Chains of MLLM (02 June, 2025)

    • soon

80. MMMC: Robust Multimodal Large Language Models Against Modality Conflict (09 July, 2025)

    • soon

81. LOTUS: A Leaderboard for Detailed Image Captioning from Quality to Societal Bias and User Preferences (25 July, 2025)

    • Hugging face link
    • soon

82. MIHBench: Benchmarking and Mitigating Multi-Image Hallucinations in Multimodal Large Language Models (01 August, 2025)

    • soon

83. HOPE: What Makes "Good" Distractors for Object Hallucination Evaluation in Large Vision-Language Models? (03 August, 2025)

    • soon

84. SHALE: A Scalable Benchmark for Fine-grained Hallucination Evaluation in LVLMs (14 August, 2025)

    • soon

85. HUmbleBench: Measuring Epistemic Humility in Multimodal Large Language Models (11 September, 2025)

    • soon

86. VHBench-10: Diving into Mitigating Hallucinations from a Vision Perspective for Large Vision-Language Models (17 September, 2025)

    • soon

87. ChartHal: A Fine-grained Framework Evaluating Hallucination of Large Vision Language Models in Chart Understanding (22 September, 2025)

    • soon

88. ColorBlindnessEval: Can Vision-Language Models Pass Color Blindness Tests? (23 September, 2025)

    • soon

89. Common-O Bench: What's in Common? Multimodal Models Hallucinate When Reasoning Across Scenes (05 November, 2025)

    • HuggingFace dataset
    • soon

90. Causal-HalBench: Causal-HalBench: Uncovering LVLMs Object Hallucinations Through Causal Intervention (13 November, 2025)

    • soon

91. What Color Is It: What Color Is It? A Text-Interference Multimodal Hallucination Benchmark (17 November, 2025)

    • soon

92. MVI-Bench: A Comprehensive Benchmark for Evaluating Robustness to Misleading Visual Inputs in LVLMs (18 November, 2025)

    • First comprehensive benchmark specifically targeting how misleading visual inputs (rather than misleading text or pure hallucination triggers) undermine LVLM robustness — fills a gap left by HallusionBench / POPE / AMBER
    • Grounded in three hierarchical levels of visual primitives: Visual Concept, Visual Attribute, Visual Relationship; curated into 6 representative categories with 1,248 expertly annotated VQA instances
    • Introduces MVI-Sensitivity, a fine-grained robustness metric that characterizes vulnerability at a granular level
    • Empirical evaluation across 18 SOTA LVLMs reveals pronounced vulnerabilities to misleading visual inputs and provides actionable insights for building more reliable LVLMs

93. PIH: Mechanisms of Prompt-Induced Hallucination in Vision-Language Models (08 January, 2026)

    • soon

94. CFHR: Once Correct, Still Wrong: Counterfactual Hallucination in Multilingual Vision-Language Models (05 February, 2026)

    • data and code will be released
    • soon

95. CSR-Bench: A Benchmark for Evaluating the Cross-modal Safety and Reliability of MLLMs (03 February, 2026)

    • soon

96. HalDec-Bench: Benchmarking Hallucination Detector in Image Captioning (16 March, 2026)

    • soon

97. FINER: MLLMs Hallucinate under Fine-grained Negative Queries (18 March, 2026)

    • soon

98. FREAK: A Fine-grained Hallucination Evaluation Benchmark for Advanced MLLMs (20 March, 2026)

    • soon

99. RSCD: Revealing Multi-View Hallucination in Large Vision-Language Models (25 March, 2026)

    • soon

100. CDH-Bench: CDH-Bench: A Commonsense-Driven Hallucination Benchmark for Evaluating Visual Fidelity in Vision-Language Models (30 March, 2026)

     • soon

101. Up to Date (31st March, 2026) and SOTA research work loading... Note: 'soon' will be replaced with brief description!

Detection

1. FPDO - Reward Model: Detecting and Preventing Hallucinations in Large Vision Language Models (AAAI 2024)

   • M-HalDetect - Hallucination detection dataset with fine-grained annotations [accurate, inaccurate and analysis]
   • Fine-grained Direct Preference Optimization (FDPO) technique and reward model trained on introduced dataset
   • High correlation of reward model score with human evaluation

2. HaELM: Evaluation and Analysis of Hallucination in Large Vision-Language Models (29 August, 2023)

   • Discussed LVLMs tendency to response as 'Yes' to judgement type queries
   • Use of ChatGPT to collect hallucination data via iterative prompt modification
   • Open-source LLM trained over this dataset for evaluation of LVLM's response
   • Evaluation results on various LVLMs, Generation length and Top-K of sampling

3. HallE-Switch: Controlling Object Hallucination in Large Vision Language Models (3 October, 2023)

   • Suggest an approach to control object existence hallucination in detailed captions of LVLM
   • Introduced CCEval which is a GPT-4 assisted evaluation method for detailed captioning (Metrics: CHAIR(i&s), Coverage, Average Length, Average Objects)
   • Detailed investigation on LVLM's component that might imfluence hallucination such as alignment of language decoder, volume of instruction data, resolution of input image and so on
   • Introduced a controlling parameters over LLMs (HallE-Control) to condition the inference of objects

4. HalluciDoctor: Mitigating Hallucinatory Toxicity in Visual Instruction Data (22 November, 2023)

   • Investigates hallucination toxicity in already existing visual instruction dataset
   • Proposed HalluciDoctor method for automatic elimination of such toxicity
   • Generation of more counterfactual instruction data with help of HalluciDoctor to improve LVLMs' resistance to hallucination

5. LogicCheckGPT: Logical Closed Loop: Uncovering Object Hallucinations in Large Vision-Language Models (18 february, 2024)

   • Postprocessing output description of LVLMs
   • 5 steps logical loop procedure such as
     • Object extraction, Object-to-Attribute inquiring, Attribute-to-Object inquiring, Logic closed llop check and Hallucination detection and mitigation
   • Experimental analysis on POPE and MME benchmark

6. UNIHD: Unified Hallucination Detection for Multimodal Large Language Models (20 February, 2024)

   • Introduce a meta evaluation benchmark called MHALUBENCH
   • Introduce a framework named UNIHD which detect modality-conflicting hallucinations at various levels such as object, attribute, and scene-text, as well as fact-conflicting hallucinations

7. Detecting and Mitigating Hallucination in Large Vision Language Models via Fine-Grained AI Feedback (22 April, 2024)

   • Use of GPT-4/GPT-4v to generate fine-grained feedback for hallucination detection and detection (by supervised finetuning (SFT) of LVLM)
   • Propose automatic pipeline for preference dataset construction
   • Hallucination Severity Aware Direct Prefential Optimization (HSA-DPO) is introduced for mitigation of LVLM's hallucination

8. MetaToken: Detecting Hallucination in Image Descriptions by Meta Classification (29 May, 2024)

   • Really cool approach
   • Lightweight method for hallucination detection

9. Image Textualization: An Automatic Framework for Creating Accurate and Detailed Image Descriptions (11 June, 2024)

   • soon

10. MediHallDetector: Detecting and Evaluating Medical Hallucinations in Large Vision Language Models (14 June, 2024)

    • Medical field hallucination detection
    • soon

11. Pelican: Correcting Hallucination in Vision-LLMs via Claim Decomposition and Program of Thought Verification (02 July, 2024)

    • soon

12. SUQ: Reference-free Hallucination Detection for Large Vision-Language Models (11 August, 2024)

    • Concluded that Supervised Uncertainity Quantification (SUQ) outperforms other reference-free hallucination detection technique such as Uncertainity-based methods and Consistency-based methods
    • An example of supervised Uncertainity Quantification method --> METATOKEN paper
    • soon

13. Pre-Training Multimodal Hallucination Detectors with Corrupted Grounding Data (30 August, 2024)

    • Proposed an approach to create corrupted grounding data which can be used to pre-train MLM hallucination detector
    • soon

14. LLMs Can Check Their Own Results to Mitigate Hallucinations in Traffic Understanding Tasks (19 September, 2024)

    • soon

15. TLDR: Token-Level Detective Reward Model for Large Vision Language Models (07 October, 2024)

    • soon

16. RadFlag: A Black-Box Hallucination Detection Method for Medical Vision Language Models (01 November, 2024)

    • soon

17. VL-Uncertainty: Detecting Hallucination in Large Vision-Language Model via Uncertainty Estimation (18 November, 2024)

    • soon

18. DHCP: Detecting Hallucinations by Cross-modal Attention Pattern in Large Vision-Language Models (27 November, 2024)

    • soon

19. Beyond Logit Lens: Contextual Embeddings for Robust Hallucination Detection & Grounding in VLMs (28 November, 2024)

    • soon

20. CutPaste&Find: Efficient Multimodal Hallucination Detector with Visual-aid Knowledge Base (18 February, 2025)

    • soon

21. Exploring Causes and Mitigation of Hallucinations in Large Vision Language Models (24 February, 2025)

    • soon

22. HalCECE: A Framework for Explainable Hallucination Detection through Conceptual Counterfactuals in Image Captioning (01 March, 2025)

    • soon

23. DASH: Detection and Assessment of Systematic Hallucinations of VLMs (30 March, 2025)

    • soon

24. ECD: Efficient Contrastive Decoding with Probabilistic Hallucination Detection - Mitigating Hallucinations in Large Vision Language Models (16 April, 2025)

    • Introduced lightweight probabilistic hallucination detector based on meta-classificationn
    • Hallucination mitigation by constracting predicted token probabilities with corresponding hallucination probabilty
    • Time efficient, lightweight and require no additional data or training of LLMs
    • Outperforms SOTA decoding techniques (VCD, ICD) for various LVLMs on different benchmarks

25. MTRE: Diverging Towards Hallucination: Detection of Failures in Vision-Language Models via Multi-token Aggregation (16 May, 2025)

    • soon

26. ZINA: Multimodal Fine-grained Hallucination Detection and Editing (16 June, 2025)

    • project page: link

27. HKD4VLM: A Progressive Hybrid Knowledge Distillation Framework for Robust Multimodal Hallucination and Factuality Detection in VLMs (17 June, 2025)

    • soon

28. PRE-HAL: Visual hallucination detection in large vision-language models via evidential conflict (24 June, 2025)

    • soon

29. NTP: Leveraging NTPs for Efficient Hallucination Detection in VLMs (20 September, 2025)

    • dataset - link
    • soon

30. GLSIM: Detecting Object Hallucinations in LVLMs via Global-Local Similarity (22 September, 2025)

    • soon

31. PAS: Prelim Attention Score for Detecting Object Hallucinations in Large Vision--Language Models (14 November, 2025)

    • soon

32. VBackChecker: Seeing is Believing: Rich-Context Hallucination Detection for MLLMs via Backward Visual Grounding (15 November, 2025)

    • soon

33. Hedge-Bench: HEDGE: Hallucination Estimation via Dense Geometric Entropy for VQA with Vision-Language Models (16 November, 2025)

    • soon

34. HalluShift++: Bridging Language and Vision through Internal Representation Shifts for Hierarchical Hallucinations in MLLMs (08 December, 2025)

    • soon

35. FaithSCAN: Model-Driven Single-Pass Hallucination Detection for Faithful Visual Question Answering (01 January, 2026)

    • soon

36. EUQ: Detecting Misbehaviors of Large Vision-Language Models by Evidential Uncertainty Quantification (05 February, 2026)

    • soon

37. VAUQ: Vision-Aware Uncertainty Quantification for LVLM Self-Evaluation (24 February, 2026)

    • soon

38. HALP: Detecting Hallucinations in Vision-Language Models without Generating a Single Token (05 March, 2026)

    • soon

39. LP: Lyapunov Probes for Hallucination Detection in Large Foundation Models (06 March, 2026)

    • soon

40. OTS: Overthinking Causes Hallucination: Tracing Confounder Propagation in Vision Language Models (CVPR 2026) (08 March, 2026)

    • Project Page
    • soon

41. CSS: Anatomy of a Lie: A Multi-Stage Diagnostic Framework for Tracing Hallucinations in Vision-Language Models (16 March, 2026)

    • soon

42. SAE: Segmentation-Based Attention Entropy: Detecting and Mitigating Object Hallucinations in Large Vision-Language Models (17 March, 2026)

    • soon

43. VLS: To See or To Please: Uncovering Visual Sycophancy and Split Beliefs in VLMs (19 March, 2026)

    • soon

44. Up to Date (31st March, 2026) and SOTA research work loading... Note: 'soon' will be replaced with brief description!

Mitigation

1. ObjMLM: Plausible May Not Be Faithful: Probing Object Hallucination in Vision-Language Pre-training (10 February 2023)

   • Deals with object hallucination problem of VLMs
   • Discuss the influence of various Vision Language Pretraining (VLP) objective (ITM, ITC and ICLM) and Image encoding methods (region-based, grid-based, and patch-based) on object hallucination
   • Introduce novel VLP objective ObjMLM to mitigate object hallucination

2. MMCoT: Multimodal Chain-of-Thought Reasoning in Language Models (17 February 2023)

   • Two stage framework by finetuning language models to perform Multimodal chain-of-thoughts (CoT) which incorporates language (text) and vision (images) modalities
   • Claims state-of-the-art performance of model under 1 billion parameters on ScienceQA benchmark
   • Multimodal-CoT has the merits of mitigating hallucination and enhancing convergence speed

3. LRV-GAVIE: Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning (26 June, 2023)

   • LRV-Instruction - positive and negative robust instruction tuning dataset with 400k visual instructions (16 tasks)
   • Negative instruction semantics: (a) Nonexistent Object Manipulation (b) Existent Object Manipulation (c) Knowledge Manipulation
   • GPT4-Assisted Visual Instruction Evaluation (GAVIE)

4. LLaVA-RLHF: Aligning Large Multimodal Models with Factually Augmented RLHF (25 September, 2023)

   • Introduced novel algorithm called Factually Augmented RLHF (Fact-RLHF) to alleviate the reward hacking phenomenon in RLHF
   • Developed evaluation benchmark MMHAL-BENCH with a special focus on penalizing hallucinations
   • Trained a LLM with RLHF (Llava-RLHF) which shows improved multimodal alignment

5. LURE: Analyzing and Mitigating Object Hallucination in Large Vision-Language Models (01 October, 2023)

   • Introduced LURE framework which is lightweight and compatible post-hoc approach for rectifying object hallucination in LVLMs
   • Statstical analysis of Co-occurence of objects, object uncertainity and object position in generated description which might correlate with object hallucination
   • Uncertain objects are put as placeholder with tokens while training LURE and while infernece (for revision)
   • Really popular method

6. HallE-Switch: Controlling Object Hallucination in Large Vision Language Models (3 October, 2023)

   • Suggest an approach to control object existence hallucination in detailed captions of LVLM
   • Introduced CCEval which is a GPT-4 assisted evaluation method for detailed captioning (Metrics: CHAIR(i&s), Coverage, Average Length, Average Objects)
   • Detailed investigation on LVLM's component that might imfluence hallucination such as alignment of language decoder, volume of instruction data, resolution of input image and so on
   • Introduced a controlling parameters over LLMs (HallE-Control) to condition the inference of objects

7. Woodpecker: Hallucination Correction for Multimodal Large Language Models (24 October, 2023)

   • Really popular method
   • Training free, post-hoc method to mitigate hallucination (but computationally expensive!!)
   • 5 steps framework:
     1. Key concept extraction from LVLM's output
     2. Formulation of questions based on key concepts
     3. Visual Knowledge validation (use of open-source object detector + pretrained VQA model)
     4. Visual claim generation (use of fix sentence templates + QA to claim model)
     5. Hallucination Correction (use LLM to correct LVLM's response)

8. VOLCANO: Mitigating Multimodal Hallucination through Self-Feedback Guided Revision (14 November, 2023)

   • soon

9. HalluciDoctor: Mitigating Hallucinatory Toxicity in Visual Instruction Data (22 November, 2023)

   • Investigates hallucination toxicity in already existing visual instruction dataset
   • Proposed HalluciDoctor method for automatic elimination of such toxicity
   • Generation of more counterfactual instruction data with help of HalluciDoctor to improve LVLMs' resistance to hallucination

10. RAH-Bench: Mitigating Hallucination in Visual Language Models with Visual Supervision (27 Novemebr, 2023)

    • soon

11. HA-DPO: Beyond Hallucinations: Enhancing LVLMs through Hallucination-Aware Direct Preference Optimization (28 November, 2023)

    • soon

12. VCD: Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding (28 November, 2023)

    • Decoding strategy
    • soon

13. OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocation (CVPR 2024)

    • soon

14. FGHE: Mitigating Fine-Grained Hallucination by Fine-Tuning Large Vision-Language Models with Caption Rewrites (04 December, 2023)

    • soon

15. RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human Feedback (01 December, 2023)

    • fine-grained refined DPO!
    • soon

16. MOCHa: Mitigating Open-Vocabulary Caption Hallucinations (06 December 2023)

    • soon

17. HACL: Hallucination Augmented Contrastive Learning for Multimodal Large Language Model (12 December 2023)

    • soon

18. SILKIE: Preference Distillation for Large Visual Language Models (17 December, 2023)

    • propose VLFeedback dataset for DPO
    • soon

19. KAM-CoT: Knowledge Augmented Multimodal Chain-of-Thoughts Reasoning (23 January, 2024)

    • soon

20. Enhancing Multimodal Large Language Models with Vision Detection Models: An Empirical Study (31 January, 2024)

    • soon

21. ViGoR: Improving Visual Grounding of Large Vision Language Models with Fine-Grained Reward Modeling (09 February, 2024)

    • soon

22. SKIP \N: A Simple Method to Reduce Hallucination in Large Vision-Language Models (12 February, 2024)

    • soon

23. MARINE: Mitigating Object Hallucination in Large Vision-Language Models via Classifier-Free Guidance (13 February, 2024)

    • soon

24. IDK-Instructions: Visually Dehallucinative Instruction Generation: Know What You Don’t Know (15 February, 2024)

    • soon

25. EFUF: Efficient Fine-grained Unlearning Framework for Mitigating Hallucinations in Multimodal Large Language Models (15 February, 2024)

    • soon

26. LogicCheckGPT: Logical Closed Loop: Uncovering Object Hallucinations in Large Vision-Language Models (18 february, 2024)

    • soon

27. POVID: Aligning Modalities in Vision Large Language Models via Preference Fine-tuning (18 february, 2024)

    • soon

28. Less is More: Mitigating Multimodal Hallucination from an EOS Decision Perspective (22 February, 2024)

    • decoding strategy
    • soon

29. CGD: Seeing is Believing: Mitigating Hallucination in Large Vision-Language Models via CLIP-Guided Decoding (23 February, 2024)

    • decoding strategy
    • soon

30. IBD: Alleviating Hallucinations in Large Vision-Language Models via Image-Biased Decoding (28 February, 2024)

    • decoding strategy
    • soon

31. HALC: Object Hallucination Reduction via Adaptive Focal-Contrast Decoding (01 March, 2024)

    • Decodig strategy to tackle object hallucination
    • soon

32. Evaluating and Mitigating Number Hallucinations in Large Vision-Language Models: A Consistency Perspective (03 March, 2024)

    • number hallucination
    • soon

33. AIT: Mitigating Dialogue Hallucination for Large Multi-modal Models via Adversarial Instruction Tuning (15 March, 2024)

    • soon

34. DVP: What if...?: Counterfactual Inception to Mitigate Hallucination Effects in Large Multimodal Models (20 March, 2024)

    • soon

35. M3ID: Multi-Modal Hallucination Control by Visual Information Grounding (20 March, 2024)

    • decoding strategy
    • soon

36. PENSIEVE: Retrospect-then-Compare Mitigates Visual Hallucination (21 March, 2024)

    • decoding strategy
    • soon

37. ESREAL: Exploiting Semantic Reconstruction to Mitigate Hallucinations in Vision-Language Models (26 March, 2024)

    • soon

38. ICD: Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding (27 March, 2024)

    • decoding strategy
    • soon

39. FGAIF: Aligning Large Vision-Language Models with Fine-grained AI Feedback (07 April, 2024)

    • soon

40. Prescribing the Right Remedy: Mitigating Hallucinations in Large Vision-Language Models via Targeted Instruction Tuning (16 April, 2024)

    • soon

41. FACT: Teaching MLLMs with Faithful, Concise and Transferable Rationales (17 April, 2024)

    • soon

42. TVP: Exploring the Transferability of Visual Prompting for Multimodal Large Language Models (17 April, 2024)

    • soon

43. TextSquare: Scaling up Text-Centric Visual Instruction Tuning (19 April, 2024)

    • soon

44. HSA-DPO: Detecting and Mitigating Hallucination in Large Vision Language Models via Fine-Grained AI Feedback (22 April, 2024)

    • Use of GPT-4/GPT-4v to generate fine-grained feedback for hallucination detection and detection (by supervised finetuning (SFT) of LVLM)
    • Propose automatic pipeline for preference dataset construction
    • Hallucination Severity Aware Direct Prefential Optimization (HSA-DPO) is introduced for mitigation of LVLM's hallucination

45. Visual Fact Checker: Enabling High-Fidelity Detailed Caption Generation (30 April - CVPR 2024)

    • soon

46. CSR: Calibrated Self-Rewarding Vision Language Models (23 May, 2024)

    • soon

47. HIO: Alleviating Hallucinations in Large Vision-Language Models through Hallucination-Induced Optimization (24 May, 2024)

    • soon

48. VDGD: Mitigating LVLM Hallucinations in Cognitive Prompts by Bridging the Visual Perception Gap (24 May, 2024)

    • soon

49. RLAIF-V: Aligning MLLMs through Open-Source AI Feedback for Super GPT-4V Trustworthines (27 May, 2024)

    • soon

50. AvisC: Don’t Miss the Forest for the Trees: Attentional Vision Calibration for Large Vision Language Models (28 May, 2024)

    • decoding strategy

51. RITUAL: Random Image Transformations as a Universal Anti-hallucination Lever in LVLMs (28 May, 2024)

    • soon

52. HALVA: Mitigating Object Hallucination via Data Augmented Contrastive Tuning (28 May, 2024)

    • decoding strategy
    • will publish code soon

53. NoiseBoost: Alleviating Hallucination with Noise Perturbation for Multimodal Large Language Models (30 May, 2024)

    • soon

54. CODE: Contrasting Self-generated Description to Combat Hallucination in Large Multi-modal Model (04 June, 2024)

    • soon

55. mDPO: Conditional Preference Optimization for Multimodal Large Language Models (17 June, 2024)

    • soon

56. DBD: Do More Details Always Introduce More Hallucinations in LVLM-based Image Captioning? (18 June, 2024)

    • Introduce novel decoding technique called Differentiated Beam Decoding (DBD)
    • soon

57. AGLA: Mitigating Object Hallucinations in Large Vision-Language Models with Assembly of Global and Local Attention (18 June, 2024)

    • Introduce AGLA, a training-free and plug-and-play decoding framework
    • soon

58. Residual Visual Decoding: Investigating and Mitigating the Multimodal Hallucination Snowballing in Large Vision-Language Models (30 June, 2024)

    • decoding method
    • Soon

59. BDHS: UNDERSTANDING ALIGNMENT IN MULTIMODAL LLMS: A COMPREHENSIVE STUDY (02 July, 2024)

    • soon

60. REVERIE: Reflective Instruction Tuning: Mitigating Hallucinations in Large Vision-Language Models (16 July, 2024) (ECCV 2024)

    • Introduced novel reflective instruction tuning to incorporate rationales into visual instruction tuning
    • Proposed large-scale instruction tuning dataset called REVERIE

61. VACoDe: Visual Augmented Contrastive Decoding (26 July, 2024)

    • decoding strategy using various visual augmentation
    • analysed effect of various visual augmentation on LVLMs performance and introduced an algorithm to select the most suitable augmentation for constractive decoding for input image
    • soon

62. PAI: Paying More Attention to Image: A Training-Free Method for Alleviating Hallucination in LVLMs (31 July, 2024) (ECCV 2024)

    • soon

63. MHR: Mitigating Multilingual Hallucination in Large Vision-Language Models (01 August, 2024)

    • soon

64. ARA: Alleviating Hallucination in Large Vision-Language Models with Active Retrieval Augmentation (01 August, 2024)

    • RAG for LVLMs for mitigating hallucination
    • soon

65. SID: Self-Introspective Decoding: Alleviating Hallucinations for Large Vision-Language Models (04 August, 2024)

    • Decoding strategy
    • Rethink constractuve decoding (CD) methods in LVLMs for hallucination mitigation
    • soon

66. LCD: Mitigating Hallucinations in Large Vision-Language Models (LVLMs) via Language-Contrastive Decoding (06 August, 2024)

    • decoding strategy to mitigate object hallucination
    • soon

67. Detect-then-Calibrate: A Comprehensive Benchmark for Relation Hallucination Evaluation, Analysis and Mitigation in Multimodal Large Language Models (18 August, 2024)

    • Proposed a novel detect-then-calibrate method to detect and mitigate hallucination
    • throshold based hallucination identification
    • hallucination rate as metric to calculate final metric called R_score

68. CLIP-DPO: Vision-Language Models as a Source of Preference for Fixing Hallucinations in LVLMs (19 August, 2024)

    • Do not require additiona training or external dataset or esemble of external LVLMs such as GPT-4
    • Use of CLIP model to prepare positive-negative pairs for DPO
    • Claims far better performance then similar work - HA-DPO with very few training data samples

69. LQCD: Towards Analyzing and Mitigating Sycophancy in Large Vision-Language Models (21 August, 2024)

    • Deals with Sycophancy in LVLMs which exists due to negative prompting
    • Introduce decoding strategy for improving LVLM's robustness toward sycophancy

70. RoVRM: A Robust Visual Reward Model Optimized via Auxiliary Textual Preference Data (22 August, 2024)

    • Introduced Robust Visula Reward model (RoVRM) to improve human-preference alignment in LVLMs
    • 3 stage progressive training and optimal transport-based preference data selection approaches to train RoVRM
    • Seemless integration with arbitrary ranking-based alignment techniques, such as direct preference optimization (DPO)

71. ConVis: Contrastive Decoding with Hallucination Visualization for Mitigating Hallucinations in Multimodal Large Language Models (25 August, 2024)

    • constractive decoding method
    • use of text-to-image (T2I) model for constractive decoding and mitigate hallucination
    • Claimed that experimental investigation on 5 benchmarks showing superior performance compared to existing techniques for hallucination mitigation

72. See or Guess: Counterfactually Regularized Image Captioning (29 August, 2024)

    • soon

73. Look, Compare, Decide: Alleviating Hallucination in Large Vision-Language Models via Multi-View Multi-Path Reasoning (30 August, 2024)

    • multi-path certainity based decoding
    • soon

74. FaithD2T Generating Faithful and Salient Text from Multimodal Data (06 September, 2024)

    • soon

75. RBD: Mitigating Hallucination in Visual-Language Models via Re-Balancing Contrastive Decoding (10 September, 2024)

    • Decoding strategy
    • soon

76. PACU: Effectively Enhancing Vision Language Large Models by Prompt Augmentation and Caption Utilization (22 September, 2024)

    • soon

77. Dentist: A Unified Hallucination Mitigation Framework for Large Vision-Language Models (24 September, 2024)

    • soon

78. TCD: Diagnosing Event Hallucinations in Video LLMs (25 September, 2024)

    • soon

79. HELPD: Mitigating Hallucination of LVLMs by Hierarchical Feedback Learning with Vision-enhanced Penalty Decoding (30 September, 2024)

    • extension of OPERA paper with vision enhanced penalty decoding
    • soon

80. PROJECTAWAY: Interpreting and Editing Vision-Language Representations to Mitigate Hallucinations (03 October, 2024)

    • soon

81. OHD-Caps: Investigating and Mitigating Object Hallucinations in Pretrained Vision-Language (CLIP) Models (04 October, 2024)

    • soon

82. LOOK TWICE BEFORE YOU ANSWER: Memory-Space Visual Retracing for Hallucination Mitigation in Multimodal Large Language Models (04 October, 2024)

    • soon

83. DAMRO: Dive into the Attention Mechanism of LVLM to Reduce Object Hallucination (06 October, 2024)

    • decoding strategy
    • soon

84. CAUSALMM: Mitigating Modality Prior-Induced Hallucinations in Multimodal Large Language Models via Deciphering Attention Causality (07 October, 2024)

    • soon

85. FROM PIXELS TO TOKENS: Revisiting Object Hallucinations in Large Vision-Language Models (09 October, 2024)

    • soon

86. VHExpansion: Automatically Generating Visual Hallucination Test Cases for Multimodal Large Language Models (15 October, 2024)

    • soon

87. SGD: Mitigating Hallucinations in Large Vision-Language Models via Summary-Guided Decoding (17 October, 2024)

    • decoding technique
    • soon

88. Fine-Grained Verifiers: Preference Modeling as Next-token Prediction in Vision-Language Alignment (18 October, 2024)

    • soon

89. MFPO: Modality-Fair Preference Optimization for Trustworthy MLLM Alignment (20 October, 2024)

    • soon (code)

90. CCA: Mitigating Object Hallucination via Concentric Causal Attention (21 October, 2024)

    • soon

91. VTI: Reducing Hallucinations in Vision-Language Models via Latent Space Steering (22 October, 2024)

    • soon

92. V-DPO: Mitigating Hallucination in Large Vision Language Models viaVision-Guided Direct Preference Optimization (05 November, 2024)

    • soon

93. EAH: Seeing Clearly by Layer Two: Enhancing Attention Heads to Alleviate Hallucination in LVLMs (15 November, 2024)

    • soon

94. HDPO: Mitigating Hallucination in Multimodal Large Language Model via Hallucination-targeted Direct Preference Optimization (15 November, 2024)

    • soon

95. Thinking Before Looking: Improving Multimodal LLM Reasoning via Mitigating Visual Hallucination (15 November, 2024)

    • soon

96. CATCH: Complementary Adaptive Token-level Contrastive Decoding to Mitigate Hallucinations in LVLMs (19 November, 2024)

    • soon

97. Looking Beyond Text: Reducing Language bias in Large Vision-Language Models via Multimodal Dual-Attention and Soft-Image Guidance (21 November, 2024)

    • project page
    • soon

98. ICT: Image-Object Cross-Level Trusted Intervention for Mitigating Object Hallucination in Large Vision-Language Models (22 November, 2024)

    • code will be released soon
    • soon

99. VaLiD: Mitigating the Hallucination of Large Vision Language Models by Visual Layer Fusion Contrastive Decoding (24 November, 2024)

    • soon

100. Devils in Middle Layers of Large Vision-Language Models: Interpreting, Detecting and Mitigating Object Hallucinations via Attention Lens (23 November, 2024)

     • soon

101. TPO: A Topic-level Self-Correctional Approach to Mitigate Hallucinations in MLLMs (26 November, 2024)

     • soon

102. WhoBrings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis (03 December, 2024)

     • soon

103. VisVM: Scaling Inference-Time Search with Vision Value Model for Improved Visual Comprehension (06 December, 2024)

     • soon

104. Verb Mirage: Unveiling and Assessing Verb Concept Hallucinations in Multimodal Large Language Models (06 December, 2024)

     • code will be published soon
     • soon

105. From Uncertainty to Trust: Enhancing Reliability in Vision-Language Models with Uncertainty-Guided Dropout Decoding (09 December, 2024)

     • soon

106. VCD Analysis: Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models (09 December, 2024)

     • soon

107. DEHALL: Combating Multimodal LLM Hallucination via Bottom-Up Holistic Reasoning (15th December, 2024)

     • soon

108. Nullu: Mitigating Object Hallucinations in Large Vision-Language Models via HalluSpace Projection (18th December, 2024)

     • soon

109. VHD: Cracking the Code of Hallucination in LVLMs with Vision-aware Head Divergence (18th December, 2024)

     • soon

110. TPO: Token Preference Optimization with Self-Calibrated Visual-Anchored Rewards for Hallucination Mitigation (19th December, 2024)

     • soon

111. Toward Robust Hyper-Detailed Image Captioning: A Multiagent Approach and Dual Evaluation Metrics for Factuality and Coverage (20th December, 2024)

     • soon

112. VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models (20th December, 2024)

     • soon

113. SENA: Beyond Human Data: Aligning Multimodal Large Language Models by Iterative Self-Evolution (20th December, 2024)

     • soon

114. IMCCD: Mitigating Hallucination for Large Vision Language Model by Inter-Modality Correlation Calibration Decoding (03 January, 2025)

     • soon

115. EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models (06 January, 2025)

     • code will be released soon

116. Socratic Questioning: Learn to Self-guide Multimodal Reasoning in the Wild (07 January, 2025)

     • soon

117. VASparse: Towards Efficient Visual Hallucination Mitigation for Large Vision-Language Model via Visual-Aware Sparsification (11 January, 2025)

     • soon

118. OPA-DPA: Mitigating Hallucinations in Large Vision-Language Models via DPO: On-Policy Data Hold the Key (16 January, 2025)

     • soon

119. MIAVLM: Mitigating Hallucinations on Object Attributes using Multiview Images and Negative Instructions (17 January, 2025)

     • soon

120. llava-fix-hallucination: Fixing Imbalanced Attention to Mitigate In-Context Hallucination of Large Vision-Language Model (21 January, 2025)

     • soon

121. CHiP: Cross-modal Hierarchical Direct Preference Optimization for Multimodal LLMs (28 January, 2025)

     • soon

122. Poison as Cure: Visual Noise for Mitigating Object Hallucinations in LVMs (31 January, 2025)

     • soon

123. MINT: Mitigating Hallucinations in Large Vision-Language Models via Token Reduction (02 February, 2025)

     • soon

124. IFCD : Mitigating Hallucinations in Large Vision-Language Models with Internal Fact-based Contrastive Decoding (03 February, 2025)

     • soon

125. UAC/DAC: Mitigating Object Hallucinations in Large Vision-Language Models via Attention Calibration (04 February, 2025)

     • soon

126. VISTA: The Hidden Life of Tokens: Reducing Hallucination of Large Vision-Language Models via Visual Information Steering (05 February, 2025)

     • soon

127. DeGF: Self-Correcting Decoding with Generative Feedback for Mitigating Hallucinations in Large Vision-Language Models (10 February, 2025)

     • soon

128. CAP: Mitigating Hallucinations in Multimodal Spatial Relations through Constraint-Aware Prompting (12 February, 2025)

     • soon

129. RE-ALIGN: Aligning Vision Language Models via Retrieval-Augmented Direct Preference Optimization (18 February, 2025)

     • soon

130. Symmetrical Visual Contrastive Optimization (S-VCO): Aligning Vision-Language Models with Minimal Contrastive Images (19 February, 2025)

     • soon

131. API Cutoff: The Role of Background Information in Reducing Object Hallucination in Vision-Language Models: Insights from Cutoff API Prompting (21 February, 2025)

     • soon

132. Exploring Causes and Mitigation of Hallucinations in Large Vision Language Models (24 February, 2025)

     • soon

133. F-CLipScore: Vision-Encoders (Already) Know What They See: Mitigating Object Hallucination via Simple Fine-Grained CLIPScore (27 February, 2025)

     • soon

134. ADAVIB: Mitigating Hallucinations in Large Vision-Language Models by Adaptively Constraining Information Flow (28 February, 2025)

     • soon

135. Octopus: Alleviating Hallucination via Dynamic Contrastive Decoding (01 March, 2025)

     • soon

136. TPC: Cross-Temporal Prediction Connection for Vision-Language Model Hallucination Reduction (06 March, 2025)

     • soon

137. Treble Counterfactual VLMs: A Causal Approach to Hallucination (06 March, 2025)

     • soon

138. PerturboLLaVA: Reducing Multimodal Hallucinations with Perturbative Visual Training (09 March, 2025)

     • soon

139. EAZY: Eliminating Hallucinations in LVLMs by Zeroing out Hallucinatory Image Tokens (10 March, 2025)

     • soon

140. Attention Reallocation: Towards Zero-cost and Controllable Hallucination Mitigation of MLLMs (12 March, 2025)

     • soon

141. Attention Hijackers: Detect and Disentangle Attention Hijacking in LVLMs for Hallucination Mitigation (14 March, 2025)

     • soon

142. Through the Magnifying Glass: Adaptive Perception Magnification for Hallucination-Free VLM Decoding (14 March, 2025

     • soon

143. TruthPrInt: Mitigating LVLM Object Hallucination Via Latent Truthful-Guided Pre-Intervention (14 March, 2025)

     • soon

144. ClearSight: Visual Signal Enhancement for Object Hallucination Mitigation in Multimodal Large Language Models (14 March, 2025)

     • soon

145. MFP: Mitigating Object Hallucinations in MLLMs via Multi-Frequency Perturbations (19 March, 2025)

     • soon
     • code release soon

146. IAVA: Instruction-Aligned Visual Attention for Mitigating Hallucinations in Large Vision-Language Models (24 March, 2025)

     • soon

147. HLPU: Mitigating Low-Level Visual Hallucinations Requires Self-Awareness: Database, Model and Training Strategy (27 March, 2025)

     • soon

148. TARAC: Mitigating Hallucination in LVLMs via Temporal Attention Real-time Accumulative Connection (05 April, 2025)

     • soon

149. Decoupling Contrastive Decoding: Robust Hallucination Mitigation in Multimodal Large Language Models (09 April, 2025)

     • code will be realesed
     • soon

150. ECD: Efficient Contrastive Decoding with Probabilistic Hallucination Detection - Mitigating Hallucinations in Large Vision Language Models (16 April, 2025)

     • Introduced lightweight probabilistic hallucination detector based on meta-classificationn
     • Hallucination mitigation by constracting predicted token probabilities with corresponding hallucination probabilty
     • Time efficient, lightweight and require no additional data or training of LLMs
     • Outperforms SOTA decoding techniques (VCD, ICD) for various LVLMs on different benchmarks

151. Generate, but Verify: Reducing Hallucination in Vision-Language Models with Retrospective Resampling (17 April, 2025)

     • soon

152. Hydra: An Agentic Reasoning Approach for Enhancing Adversarial Robustness and Mitigating Hallucinations in Vision-Language Models (19 April, 2025)

     • soon

153. BBVPE: Black-Box Visual Prompt Engineering for Mitigating Object Hallucination in Large Vision Language Models (30 April, 2025)

     • soon

154. TTA Framework: Mitigating Image Captioning Hallucinations in Vision-Language Models (06 May, 2025)

     • soon

155. Critique Before Thinking: Mitigating Hallucination through Rationale-Augmented Instruction Tuning (12 May, 2025)

     • soon

156. DCLA: Mitigating Hallucinations via Inter-Layer ConsistencyAggregation in Large Vision-Language Models (18 May, 2025)

     • soon

157. CICD: Cross-Image Contrastive Decoding: Precise, Lossless Suppression of Language Priors in Large Vision-Language Models (20 May, 2025)

     • code will be released soon
     • decoding strategy

158. SEVI: Aligning Attention Distribution to Information Flow for Hallucination Mitigation in Large Vision-Language Models (20 May, 2025)

     • code will be released soon

159. OViP: Online Vision-Language Preference Learning (21 May, 2025)

     • soon

160. SSL: Steering LVLMs via Sparse Autoencoder for Hallucination Mitigation (22 May, 2025)

     • soon

161. SPIN: Mitigating Hallucinations in Vision-Language Models through Image-Guided Head Suppression (22 May, 2025)

     • soon

162. ED: Do You Keep an Eye on What I Ask? Mitigating Multimodal Hallucination via Attention-Guided Ensemble Decoding (23 May, 2025)

     • soon

163. VaLSe Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations (23 May, 2025)

     • soon

164. CGC: Image Tokens Matter: Mitigating Hallucination in Discrete Tokenizer-based Large Vision-Language Models via Latent Editing (24 May, 2025)

     • soon

165. EVRB: Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models (26 May, 2025)

     • soon

166. C-PMI: Grounding Language with Vision: A Conditional Mutual Information Calibrated Decoding Strategy for Reducing Hallucinations in LVLMs (26 May, 2025)

     • soon

167. CAAC: Mitigating Hallucination in Large Vision-Language Models via Adaptive Attention Calibration (27 May, 2025)

     • soon

168. RVCD: Retrieval Visual Contrastive Decoding to Mitigate Object Hallucinations in Large Vision-Language Models (29 May, 2025)

     • soon

169. BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models (30 May, 2025)

     • project page: link

170. *CLAIM: Mitigating Multilingual Object Hallucination in Large Vision-Language Models with Cross-Lingual Attention Intervention (03 June, 2025)

     • soon

171. EMPO: Mitigating Hallucinations in Large Vision-Language Models via Entity-Centric Multimodal Preference Optimization (04 June, 2025)

     • soon

172. When Semantics Mislead Vision: Mitigating Large Multimodal Models Hallucinationsin Scene Text Spotting and Understanding (05 June, 2025)

     • soon

173. LPS: Mitigating Object Hallucination via Robust Local Perception Search (07 June, 2025)

     • soon

174. Seeing Far and Clearly: Mitigating Hallucinations in MLLMs with Attention Causal Decoding (07 June, 2025)

     • soon
     • Project page: link

175. SHE: Mitigating Behavioral Hallucination in Multimodal Large Language Models for Sequential Images (08 June, 2025)

     • soon

176. SECOND: Mitigating Perceptual Hallucination in Vision-Language Models via Selective and Contrastive Decoding (10 June, 2025)

     • soon

177. MoD: Mixture of Decoding: An Attention-Inspired Adaptive Decoding Strategy to Mitigate Hallucinations in Large Vision-Language Models (10 June, 2025)

     • soon

178. ReVisiT: Revisit What You See: Disclose Language Prior in Vision Tokens for Efficient Guided Decoding of LVLMs (11 June, 2025)

     • soon

179. HalLoc: Token-level Localization of Hallucinations for Vision Language Models (12 June, 2025)

     • soon

180. TL-DPO: Stop learning it all to mitigate visual hallucination, Focus on the hallucination target. (13 June, 2025)

     • soon

181. TAI & HAI: Not All Tokens and Heads Are Equally Important: Dual-Level Attention Intervention for Hallucination Mitigation (14 June, 2025)

     • soon

182. ASCD: Attention-Steerable Contrastive Decoding for Reducing Hallucination in MLLM (17 June, 2025)

     • soon

183. HalluRNN: Mitigating Hallucinations via Recurrent Cross-Layer Reasoning in Large Vision-Language Models (21 June, 2025)

     • soon

184. MDSAM: Memory-Driven Sparse Attention Matrix for LVLMs Hallucination Mitigation (21 June, 2025)

     • soon

185. PostAlign: Multimodal Grounding as a Corrective Lens for MLLMs (22 June, 2025)

     • soon

186. GRPO: Seeing is Believing? Mitigating OCR Hallucinations in Multimodal Large Language Models (25 June, 2025)

     • soon

187. DLC: Mitigating Hallucination of Large Vision-Language Models via Dynamic Logits Calibration (26 June, 2025)

     • code will be released soon

188. ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models (27 June, 2025)

     • soon

189. Preemptive Hallucination Reduction: An Input-Level Approach for Multimodal Language Model (27 June, 2025)

     • soon

190. CAI: Caption-Sensitive Attention Intervention for Mitigating Object Hallucination in Large Vision-Language Models (30 June, 2025)

     • soon

191. ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models (01 July, 2025)

     • soon

192. SEED: Identify, Isolate, and Purge: Mitigating Hallucinations in LVLMs via Self-Evolving Distillation (07 July, 2025)

     • soon

193. ReLoop: "Seeing Twice and Thinking Backwards" via Closed-loop Training to Mitigate Hallucinations in Multimodal understanding (07 July, 2025)

     • code will be released soon

194. Energy-Guided Decoding for Object Hallucination Mitigation (10 July, 2025)

     • soon

195. SENTINEL: Mitigating Object Hallucinations via Sentence-Level Early Intervention (16 July, 2025)

     • soon

196. EVA: Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models (21 Jily, 2025)

     • soon

197. INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling (22 July, 2025)

     • soon

198. MCA-LLaVA: Manhattan Causal Attention for Reducing Hallucination in Large Vision-Language Models (23 July, 2025)

     • soon

199. LISA: A Layer-wise Integration and Suppression Approach for Hallucination Mitigation in Multimodal Large Language Models (25 July, 2025)

     • soon

200. SENITEL: Mitigating Object Hallucinations via Sentence-Level Early Intervention (26 July, 2025)

     • soon

201. ViHallu: See Different, Think Better: Visual Variations Mitigating Hallucinations in LVLMs (30 July, 2025)

     • soon

202. MAP: Mitigating Hallucinations in Large Vision-Language Models with Map-Level Attention Processing (03 August, 2025) - soon

203. Prompt-in-Image: Cure or Poison? Embedding Instructions Visually Alters Hallucination in Vision-Language Models (03 August, 2025) - soon

204. Modality Bias in LVLMs: Analyzing and Mitigating Object Hallucination via Attention Lens (04 August, 2025) - soon

205. SAVER: Mitigating Hallucinations in Large Vision-Language Models via Style-Aware Visual Early Revision (05 August, 2025) - soon

206. IKOD: Mitigating Visual Attention Degradation in Large Vision-Language Models (05 August, 2025) - soon

207. Hacking Hallucinations of MLLMs with Causal Sufficiency and Necessity (06 August, 2025) - soon

208. Obliviate: Analyzing and Mitigating Object Hallucination: A Training Bias Perspective (06 August, 2025) - code soon

209. TARS: MinMax Token-Adaptive Preference Strategy for MLLM Hallucination Reduction (09 August, 2025)

     • soon

210. MRFD: Multi-Region Fusion Decoding with Self-Consistency for Mitigating Hallucinations in LVLMs (14 August, 2025)

     • soon

211. MRGD: Controlling Multimodal LLMs via Reward-guided Decoding (15 August, 2025)

     • soon

212. CHAIR-DPO: Mitigating Hallucinations in Multimodal LLMs via Object-aware Preference Optimization (27 August, 2025)

     • soon

213. VPFC:Two Causes, Not One: Rethinking Omission and Fabrication Hallucinations in MLLMs (30 August, 2025)

     • soon

214. D-LEAF: Localizing and Correcting Hallucinations in Multimodal LLMs via Layer-to-head Attention Diagnostics (09 September, 2025)

     • soon

215. APASI: Mitigating Hallucinations in Large Vision-Language Models by Self-Injecting Hallucinations (14 September, 2025)

     • soon

216. VisionWeaver: Diving into Mitigating Hallucinations from a Vision Perspective for Large Vision-Language Models (17 September, 2025)

     • soon

217. Exposing Hallucinations To Suppress Them: VLMs Representation Editing With Generative Anchors (26 September, 2025)

     • code soon

218. Self-Consistency as a Free Lunch: Reducing Hallucinations in Vision-Language Models via Self-Reflection (27 September, 2025)

     • soon

219. SCPO: Mitigating Visual Hallucinations via Semantic Curriculum Preference Optimization in MLLMs (29 September, 2025)

     • soon

220. LayerCD: Mitigating Hallucination in Multimodal LLMs with Layer Contrastive Decoding (29 September, 2025)

     • soon

221. GACD: Mitigating Multimodal Hallucinations via Gradient-based Self-Reflection (01 October, 2025)

     • soon

222. MaskCD: Mitigating LVLM Hallucinations by Image Head Masked Contrastive Decoding (03 October, 2025)

     • soon

223. ChainMPQ: Interleaved Text-Image Reasoning Chains for Mitigating Relation Hallucinations (07 October, 2025)

     • soon

224. On Epistemic Uncertainty of Visual Tokens for Object Hallucinations in Large Vision-Language Models (10 October, 2025)

     • soon

225. Functional Attention Control: Mitigating Hallucination in Multimodal Reasoning via Functional Attention Control (11 October, 2025)

     • project link
     • soon

226. CMG: When Images Speak Louder: Mitigating Language Bias-induced Hallucinations in VLMs through Cross-Modal Guidance (12 October, 2025)

     • soon

227. OCR Guided Deoding: Vision Language Models Map Logos to Text via Semantic Entanglement in the Visual Projector (14 October, 2025)

     • soon

228. SAVCD: Self-Augmented Visual Contrastive Decoding (15 October, 2025)

     • soon

229. TCD: Watermarking for Factuality: Guiding Vision-Language Models Toward Truth via Tri-layer Contrastive Decoding (16 October, 2025)

     • soon

230. SHIELD: Suppressing Hallucinations In LVLM Encoders via Bias and Vulnerability Defense (18 October, 2025)

     • code will be released soon
     • soon

231. TITA: Token-Level Inference-Time Alignment for Vision-Language Models (20 October, 2025)

     • soon

232. ATED: Beyond Single Models: Mitigating Multimodal Hallucinations via Adaptive Token Ensemble Decoding (21 October, 2025)

     • soon

233. PruneHal: PruneHal: Reducing Hallucinations in Multi-modal Large Language Models through Adaptive KV Cache Pruning (22 October, 2025)

     • code will release soon
     • soon

234. Induce-Detect-Suppress: Why LVLMs Are More Prone to Hallucinations in Longer Responses: The Role of Context (23 October, 2025)

     • soon

235. Generating Accurate and Detailed Captions for High-Resolution Images (31 October, 2025)

     • soon

236. VisAlign: Towards Mitigating Hallucinations in Large Vision-Language Models by Refining Textual Embeddings (07 November, 2025)

     • soon

237. IRI: Causal Tracing of Object Representations in Large Vision Language Models: Mechanistic Interpretability and Hallucination Mitigation (08 November, 2025)

     • soon

238. GIFT: Capturing Gaze Shifts for Guidance: Cross-Modal Fusion Enhancement for VLM Hallucination Mitigation (10 November, 2025)

     • soon

239. VTACR: Causally-Grounded Dual-Path Attention Intervention for Object Hallucination Mitigation in LVLMs (12 November, 2025)

     • soon

240. TACO: Taming Object Hallucinations with Verified Atomic Confidence Estimation (12 November, 2025)

     • soon

241. RUDDER: Adaptive Residual-Update Steering for Low-Overhead Hallucination Mitigation in Large Vision Language Models (13 November, 2025)

     • code base
     • soon

242. SRF: Suppressing VLM Hallucinations with Spectral Representation Filtering (15 November, 2025)

     • soon

243. Intervene-All-Paths: Unified Mitigation of LVLM Hallucinations across Alignment Formats (21 November, 2025)

     • soon

244. AGE-VLM: Attention Guided Alignment in Efficient Vision-Language Models (21 November, 2025)

     • soon

245. VGA: Tell Model Where to Look: Mitigating Hallucinations in MLLMs by Vision-Guided Attention (25 November, 2025) - soon

246. Optimizing LVLMs with On-Policy Data for Effective Hallucination Mitigation (30 November, 2025) - soon

247. InEx: Hallucination Mitigation via Introspection and Cross-Modal Multi-Agent Collaboration (02 December, 2025) - soon

248. V-ITI: Mitigating Hallucinations in Multimodal Large Language Models via Visual Inference-Time Intervention (03 December, 2025) - soon

249. SANTA: Mitigating Object and Action Hallucinations in Multimodal LLMs via Self-Augmented Contrastive Alignment (04 December, 2025) - soon

250. CG-VLM: Conscious Gaze: Adaptive Attention Mechanisms for Hallucination Mitigation in Vision-Language Models (05 December, 2025) - soon

251. Toward More Reliable Artificial Intelligence: Reducing Hallucinations in Vision-Language Models (08 December, 2025) - soon

252. SAVE: Sparse Autoencoder-Driven Visual Information Enhancement for Mitigating Object Hallucination (08 December, 2025) - soon

253. VEGAS: VEGAS: Mitigating Hallucinations in Large Vision-Language Models via Vision-Encoder Attention Guided Adaptive Steering (12 December, 2025) - soon

254. VDC: Revealing Perception and Generation Dynamics in LVLMs: Mitigating Hallucinations via Validated Dominance Correction (21 December, 2025) - soon

255. HDD: Watch Closely: Mitigating Object Hallucinations in Large Vision-Language Models with Disentangled Decoding (22 December, 2025) - soon

256. ALEAHallu: Look Closer! An Adversarial Parametric Editing Framework for Hallucination Mitigation in VLMs (26 December, 2025) - soon

257. CoFi-Dec: CoFi-Dec: Hallucination-Resistant Decoding via Coarse-to-Fine Generative Feedback in Large Vision-Language Models (29 December, 2025) - soon

258. DA-DPO: Cost-efficient Difficulty-aware Preference Optimization for Reducing MLLM Hallucinations (02 January, 2026) - soon

259. CRoPS: A Training-Free Hallucination Mitigation Framework for Vision-Language Models (02 January, 2026) - soon

260. AFTER: Mitigating the Object Hallucination of LVLM via Adaptive Factual-Guided Activation Editing (05 January, 2026) - soon

261. TGIF: Text-Guided Layer Fusion Mitigates Hallucination in Multimodal LLMs (06 January, 2026) - soon

262. SDCD: Structure-Disrupted Contrastive Decoding for Mitigating Hallucinations in Large Vision-Language Models (07 January, 2026) - soon

263. VLI: Vision-Language Introspection: Mitigating Overconfident Hallucinations in MLLMs via Interpretable Bi-Causal Steering (08 January, 2026) - soon

264. VIB-Probe: VIB-Probe: Detecting and Mitigating Hallucinations in Vision-Language Models via Variational Information Bottleneck (09 January, 2026) - code will be released - soon

265. NTK: Ground What You See: Hallucination-Resistant MLLMs via Caption Feedback, Diversity-Aware Sampling, and Conflict Regularization (09 January, 2026)

     • soon

266. CEI: Context-Aware Decoding for Faithful Vision-Language Generation (09 January, 2026)

     • soon

267. ACG: Attention-space Contrastive Guidance for Efficient Hallucination Mitigation in LVLMs (20 January, 2026)

     • soon

268. SARE: Beyond Superficial Unlearning: Sharpness-Aware Robust Erasure of Hallucinations in Multimodal LLMs (23 January, 2026)

     • soon

269. LVLMs-Saliency: Hallucination Begins Where Saliency Drops (28 January, 2026)

     • soon

270. Self-Validation Framework: Countering the Over-Reliance Trap: Mitigating Object Hallucination for LVLMs via a Self-Validation Framework (30 January, 2026)

     • soon

271. OSGA: One-shot Optimized Steering Vector for Hallucination Mitigation for VLMs (30 January, 2026)

     • soon

272. CNS: Towards Interpretable Hallucination Analysis and Mitigation in LVLMs via Contrastive Neuron Steering (31 January, 2026)

     • soon

273. ResDec: Residual Decoding: Mitigating Hallucinations in Large Vision-Language Models via History-Aware Residual Guidance (01 February, 2026)

     • soon

274. MACD: Model-Aware Contrastive Decoding via Counterfactual Data (02 February, 2026)

     • code and data will be released soon
     • soon

275. KVSmooth: KVSmooth: Mitigating Hallucination in Multi-modal Large Language Models through Key-Value Smoothing (04 February, 2026)

     • soon

276. LLL: Logit Lens Supervision for Patch-Level Explanations in Vision-Language Models (02 February, 2026)

     • soon

277. IRIS: Implicit Reward-Guided Internal Sifting for Mitigating Multimodal Hallucination (02 February, 2026)

     • soon

278. LASER: Beyond Static Cropping: Layer-Adaptive Visual Localization and Decoding Enhancement (04 February, 2026)

     • soon

279. SAKED: Mitigating Hallucination in Large Vision-Language Models via Stability-Aware Knowledge Enhanced Decoding (10 February, 2026)

     • soon

280. Scalpel: Fine-Grained Alignment of Attention Activation Manifolds via Mixture Gaussian Bridges to Mitigate Multimodal Hallucination (10 February, 2026)

     • soon

281. SchröMind: Mitigating Hallucinations in Multimodal Large Language Models via Solving the Schrödinger Bridge Problem (10 February, 2026)

     • soon

282. VALE: Attention to details, logits to truth: visual-aware attention and logits enhancement to mitigate hallucinations in LVLMs (10 February, 2026)

     • soon

283. HII-DPO: Eliminate Hallucination via Accurate Hallucination-Inducing Counterfactual Images (11 February, 2026)

     • soon

284. REVIS: Sparse Latent Steering to Mitigate Object Hallucination in Large Vision-Language Models (12 February, 2026)

     • soon

285. OAVCD: Mask What Matters: Mitigating Object Hallucinations in Multimodal Large Language Models with Object-Aligned Visual Contrastive Decoding (12 February, 2026)

     • soon

286. SAVAA: Mitigating Hallucinations in LVLMs via Step-wise Adaptive Visual Attention Amplification (14 February, 2026)

     • soon

287. PADE: Revealing and Enhancing Core Visual Regions: Harnessing Internal Attention Dynamics for Hallucination Mitigation in LVLMs (17 February, 2026)

     • soon

288. MIRROR: Bridging Modality Disconnect in Self-Reflection via Closed-Loop Visually Grounded Verification (21 February, 2026)

     • soon

289. HIME: Mitigating Object Hallucinations in LVLMs via Hallucination Insensitivity Model Editing (21 February, 2026)

     • soon

290. CDHR: Causal Decoding for Hallucination-Resistant Multimodal Large Language Models (24 February, 2026)

     • soon

291. SCR: Beyond Dominant Patches: Spatial Credit Redistribution For Grounded Vision-Language Models (25 February, 2026)

     • soon

292. NoLan: Mitigating Object Hallucinations in Large Vision-Language Models via Dynamic Suppression of Language Priors (25 February, 2026)

     • soon

293. DMAS: Dynamic Multimodal Activation Steering for Hallucination Mitigation in Large Vision-Language Models (25 February, 2026)

     • soon

294. HulluEdit: Single-Pass Evidence-Consistent Subspace Editing for Mitigating Hallucinations in Large Vision-Language Models (26 February, 2026)

     • soon

295. AIR: Look Carefully: Adaptive Visual Reinforcements in Multimodal Large Language Models for Hallucination Mitigation (27 February, 2026)

     • soon

296. ICLA: Self-Correction Inside the Model: Leveraging Layer Attention to Mitigate Hallucinations in Large Vision Language Models (28 February, 2026)

     • soon

297. AdaIAT: Adaptively Increasing Attention to Generated Text to Alleviate Hallucinations in LVLM (05 March, 2026)

     • soon

298. GRANULON: Awakening Pixel-Level Visual Encoders with Adaptive Multi-Granularity Semantics for MLLM (09 March, 2026)

     • soon

299. GC: GroundCount: Grounding Vision-Language Models with Object Detection for Mitigating Counting Hallucinations (11 March, 2026)

     • soon

300. CIPHER: Fighting Hallucinations with Counterfactuals: Diffusion-Guided Perturbations for LVLM Hallucination Suppression (11 March, 2026)

     • soon

301. VTM: One Token, Two Fates: A Unified Framework via Vision Token Manipulation Against MLLMs Hallucination (11 March, 2026)

     • soon

302. Kestrel: Grounding Self-Refinement for LVLM Hallucination Mitigation (17 March, 2026)

     • soon

303. LTS-FS: Locate-then-Sparsify: Attribution Guided Sparse Strategy for Visual Hallucination Mitigation (17 March, 2026)

     • soon

304. AIR: Mitigating Object Hallucinations in LVLMs via Attention Imbalance Rectification (25 March, 2026)

     • soon

305. CLVA: Visual Attention Drifts,but Anchors Hold:Mitigating Hallucination in Multimodal Large Language Models via Cross-Layer Visual Anchors (26 March, 2026)

     • soon

306. ClipTTT: CLIP-Guided Test-Time Training Helps LVLMs See Better (27 March, 2026)

     • soon

307. SAGE: Sink-Aware Grounded Decoding for Multimodal Hallucination Mitigation (29 March, 2026)

     • soon

308. HIRE: Hallucination-aware intermediate representation edit in large vision-language models (31 March, 2026)

     • soon

309. Up to Date (31st March, 2026) and SOTA research work loading... Note: 'soon' will be replaced with brief description!

Survey

1. DEEP LEARNING APPROACHES ON IMAGE CAPTIONING: A REVIEW (22 August, 2023)
2. A Survey on Hallucination in Large Vision-Language Models (1 February, 2024)
3. Visual Hallucination: Definition, Quantification, and Prescriptive Remediations (26 March, 2024)
4. Hallucination of Multimodal Large Language Models: A Survey (29 April, 2024)
5. Unveiling Hallucination in Text, Image, Video, and Audio Foundation Models: A Comprehensive Survey (20 May, 2024)
6. Benchmark Evaluations, Applications, and Challenges of Large Vision Language Models: A Survey (04 January 2025)
7. Image Captioning Evaluation in the Age of Multimodal LLMs: Challenges and Future Perspectives (18 March, 2025)
8. Exploring Hallucination of Large Multimodal Models in Video Understanding: Benchmark, Analysis and Mitigation (25 March, 2025)
9. The Mirage of Performance Gains: Why Contrastive Decoding Fails to Address Multimodal Hallucination (18 April, 2025)
10. A Survey of Multimodal Hallucination Evaluation and Detection (25 July, 2025)
11. Review of Hallucination Understanding in Large Language and Vision Models (26 September, 2025)
12. Up to Date (08 february, 2026) and SOTA research work loading... Note: 'soon' will be replaced with brief description!