mlsanityData to 7 Oct 2026

HalluciDoctor: Mitigating Hallucinatory Toxicity in Visual Instruction Data

arXiv 2311.13614 ↗First posted 22 Nov 2023cs.CV cs.AIcited by 124

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

Abstract (arXiv)

Multi-modal Large Language Models (MLLMs) tuned on machine-generated instruction-following data have demonstrated remarkable performance in various multi-modal understanding and generation tasks. However, the hallucinations inherent in machine-generated data, which could lead to hallucinatory outputs in MLLMs, remain under-explored. This work aims to investigate various hallucinations (i.e., object, relation, attribute hallucinations) and mitigate those hallucinatory toxicities in large-scale machine-generated visual instruction datasets. Drawing on the human ability to identify factual errors, we present a novel hallucination detection and elimination framework, HalluciDoctor, based on the cross-checking paradigm. We use our framework to identify and eliminate hallucinations in the training data automatically. Interestingly, HalluciDoctor also indicates that spurious correlations arising from long-tail object co-occurrences contribute to hallucinations. Based on that, we execute counterfactual visual instruction expansion to balance data distribution, thereby enhancing MLLMs' resistance to hallucinations. Comprehensive experiments on hallucination evaluation benchmarks show that our method successfully mitigates 44.6% hallucinations relatively and maintains competitive performance compared to LLaVA. The data and code for this paper are publicly available. urlhttps://github.com/Yuqifan1117/HalluciDoctor.

Key facts. Cited by 124 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 45 of its 57 references that are in the map. First posted on arXiv 22 Nov 2023.

Builds on

45 of 57 references.

Closest ancestors · 4

Same direction, newest first

  1. 2023Woodpecker: Hallucination Correction for Multimodal Large Language Models24 Oct
  2. 2023Analyzing and Mitigating Object Hallucination in Large Vision-Language Models1 Oct
  3. 2023Detecting and Preventing Hallucinations in Large Vision Language Models11 Aug
  4. 2023Evaluating Object Hallucination in Large Vision-Language Models17 May

Other references · 41

  1. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  3. 2022Training language models to follow instructions with human feedbackcited by 15,712
  4. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  5. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  6. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  7. 2023Visual Instruction Tuningcited by 7,792
  8. 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Modelscited by 6,069
  9. 2022Flamingo: a Visual Language Model for Few-Shot Learningcited by 4,337
  10. 2022BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generationcited by 4,310
  11. 2023Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detectioncited by 3,274
  12. 2023InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuningcited by 2,855
29 more
  1. 2023MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Modelscited by 2,689
  2. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,674
  3. 2016Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotationscited by 2,646
  4. 2023A Survey of Large Language Modelscited by 2,537
  5. 2023MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Modelscited by 1,624
  6. 2019OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledgecited by 1,114
  7. 2023mPLUG-Owl: Modularization Empowers Large Language Models with Multimodalitycited by 1,038
  8. 2023A Survey on Multimodal Large Language Modelscited by 898
  9. 2023Instruction Tuning with GPT-4cited by 676
  10. 2018Object Hallucination in Image Captioningcited by 661
  11. 2021Multimodal Few-Shot Learning with Frozen Language Modelscited by 657
  12. 2023Otter: A Multi-Modal Model with In-Context Instruction Tuningcited by 548
  13. 2018nocaps: novel object captioning at scalecited by 444
  14. 2023AlignScore: Evaluating Factual Consistency with a Unified Alignment Functioncited by 284
  15. 2023Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Taskscited by 243
  16. 2020Deep Structural Causal Models for Tractable Counterfactual Inferencecited by 210
  17. 2022From Images to Textual Prompts: Zero-shot VQA with Frozen Large Language Modelscited by 114
  18. 2020Counterfactual Data Augmentation using Locally Factored Dynamicscited by 93
  19. 2023VIGC: Visual Instruction Generation and Correctioncited by 80
  20. 2023Fine-tuning Multimodal LLMs to Follow Zero-shot Demonstrative Instructionscited by 76
  21. 2022Tomayto, Tomahto. Beyond Token-level Answer Equivalence for Question Answering Evaluationcited by 66
  22. 2023FactKB: Generalizable Factuality Evaluation using Language Models Enhanced with Factual Knowledgecited by 55
  23. 2023FACTUAL: A Benchmark for Faithful and Consistent Textual Scene Graph Parsingcited by 50
  24. 2023COCO-Counterfactuals: Automatically Constructed Counterfactual Examples for Image-Text Pairscited by 39
  25. 2023Visually-Prompted Language Model for Fine-Grained Scene Graph Generation in an Open Worldcited by 35
  26. 2022Incorporating Bias-aware Margins into Contrastive Loss for Collaborative Filteringcited by 29
  27. 2021MAGIC: Multimodal relAtional Graph adversarIal inferenCe for Diverse and Unpaired Text-based Image Captioningcited by 27
  28. 2023Learning in Imperfect Environment: Multi-Label Classification with Long-Tailed Distribution and Partial Labelscited by 17
  29. 2023Interactive Data Synthesis for Systematic Vision Adaptation via LLMs-AIGCs Collaborationcited by 12

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 38; 2025: 64; 2026: 22.

124 papers.

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  1. 2024Hallucination of Multimodal Large Language Models: A Surveycited by 380
  2. 2024AnyEdit: Mastering Unified High-Quality Image Editing for Any Ideacited by 235
  3. 2024Devils in Middle Layers of Large Vision-Language Models: Interpreting, Detecting and Mitigating Object Hallucinations via Attention Lenscited by 139
  4. 2024IBD: Alleviating Hallucinations in Large Vision-Language Models via Image-Biased Decodingcited by 118
  5. 2024Self-Introspective Decoding: Alleviating Hallucinations for Large Vision-Language Modelscited by 90
  6. 2024Calibrated Self-Rewarding Vision Language Modelscited by 77
  7. 2024Look Twice Before You Answer: Memory-Space Visual Retracing for Hallucination Mitigation in Multimodal Large Language Modelscited by 72
  8. 2024VidHalluc: Evaluating Temporal Hallucinations in Multimodal Large Language Models for Video Understandingcited by 65
  9. 2024CogCoM: A Visual Language Model with Chain-of-Manipulations Reasoningcited by 64
  10. 2025The Hidden Life of Tokens: Reducing Hallucination of Large Vision-Language Models via Visual Information Steeringcited by 62
  11. 2024EventHallusion: Diagnosing Event Hallucinations in Video LLMscited by 55
  12. 2025LLMDet: Learning Strong Open-Vocabulary Object Detectors under the Supervision of Large Language Modelscited by 53
38 more
  1. 2025LLM-based Agents Suffer from Hallucinations: A Survey of Taxonomy, Methods, and Directionscited by 49
  2. 2025DyFo: A Training-Free Dynamic Focus Visual Search for Enhancing LMMs in Fine-Grained Visual Understandingcited by 47
  3. 2024Don't Miss the Forest for the Trees: Attentional Vision Calibration for Large Vision Language Modelscited by 46
  4. 2025Seeing Far and Clearly: Mitigating Hallucinations in MLLMs with Attention Causal Decodingcited by 42
  5. 2025Distraction is All You Need for Multimodal Large Language Model Jailbreakingcited by 31
  6. 2024Unveiling the Ignorance of MLLMs: Seeing Clearly, Answering Incorrectlycited by 29
  7. 2024HumanEdit: A High-Quality Human-Rewarded Dataset for Instruction-based Image Editingcited by 29
  8. 2025Limitations of Large Language Models in Clinical Problem-Solving Arising from Inflexible Reasoningcited by 29
  9. 2024ICT: Image-Object Cross-Level Trusted Intervention for Mitigating Object Hallucination in Large Vision-Language Modelscited by 28
  10. 2025Large Vision-Language Model Alignment and Misalignment: A Survey Through the Lens of Explainabilitycited by 28
  11. 2025Generative Multimodal Pretraining with Discrete Diffusion Timestep Tokenscited by 27
  12. 2025A Survey of Multimodal Hallucination Evaluation and Detectioncited by 26
  13. 2025Towards Agentic Recommender Systems in the Era of Multimodal Large Language Modelscited by 25
  14. 2025Mitigating Object Hallucinations in Large Vision-Language Models via Attention Calibrationcited by 23
  15. 2025Octopus: Alleviating Hallucination via Dynamic Contrastive Decodingcited by 23
  16. 2025Cross-Modal Attention Calibration for LVLM Hallucination Mitigationcited by 21
  17. 2025Exploring Hallucination of Large Multimodal Models in Video Understanding: Benchmark, Analysis and Mitigationcited by 20
  18. 2025M2-omni: Advancing Omni-MLLM for Comprehensive Modality Support with Competitive Performancecited by 19
  19. 2024Towards Unified Multimodal Editing with Enhanced Knowledge Collaborationcited by 18
  20. 2025Robust Multimodal Large Language Models Against Modality Conflictcited by 17
  21. 2024Interpreting and Mitigating Hallucination in MLLMs through Multi-agent Debatecited by 16
  22. 2024Unified Generative and Discriminative Training for Multi-modal Large Language Modelscited by 15
  23. 2025Mirage in the Eyes: Hallucination Attack on Multi-modal Large Language Models with Only Attention Sinkcited by 15
  24. 2025PaMi-VDPO: Mitigating Video Hallucinations by Prompt-Aware Multi-Instance Video Preference Learningcited by 15
  25. 2025ScaleCap: Inference-Time Scalable Image Captioning via Dual-Modality Debiasingcited by 15
  26. 2024Improving Large Models with Small models: Lower Costs and Better Performancecited by 14
  27. 2025Hallucinatory Image Tokens: A Training-free EAZY Approach on Detecting and Mitigating Object Hallucinations in LVLMscited by 14
  28. 2024Certainly Uncertain: A Benchmark and Metric for Multimodal Epistemic and Aleatoric Awarenesscited by 13
  29. 2024Reflective Instruction Tuning: Mitigating Hallucinations in Large Vision-Language Modelscited by 12
  30. 2025Patch Matters: Training-free Fine-grained Image Caption Enhancement via Local Perceptioncited by 12
  31. 2025When Semantics Mislead Vision: Mitigating Large Multimodal Models Hallucinations in Scene Text Spotting and Understandingcited by 12
  32. 2025MCA-LLaVA: Manhattan Causal Attention for Reducing Hallucination in Large Vision-Language Modelscited by 11
  33. 2024Mastering Collaborative Multi-modal Data Selection: A Focus on Informativeness, Uniqueness, and Representativenesscited by 10
  34. 2024NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queriescited by 10
  35. 2025Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perceptioncited by 10
  36. 2025Mitigating Hallucinations in Large Vision-Language Models via Entity-Centric Multimodal Preference Optimizationcited by 10
  37. 2025INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Samplingcited by 10
  38. 2024NoiseBoost: Alleviating Hallucination with Noise Perturbation for Multimodal Large Language Modelscited by 9

50 most cited of 124 All 124 (JSON)

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  1. 2024Detecting and Mitigating Hallucination in Large Vision Language Models via Fine-Grained AI Feedback
  2. 2026HalluScope: Fine-grained Hallucination Diagnosis for Multimodal Large Language Models
  3. 2025Generate, but Verify: Reducing Hallucination in Vision-Language Models with Retrospective Resampling
  4. 2025Theoretical Foundations and Mitigation of Hallucination in Large Language Models
  5. 2025Seeing is Believing: Rich-Context Hallucination Detection for MLLMs via Backward Visual Grounding
  6. 2026Scalable Token-Level Hallucination Detection in Large Language Models
  7. 2025Analyzing and Mitigating Object Hallucination: A Training Bias Perspective
  8. 2024ODE: Open-Set Evaluation of Hallucinations in Multimodal Large Language Models