mlsanityData to 14 Oct 2026

MIHBench: Benchmarking and Mitigating Multi-Image Hallucinations in Multimodal Large Language Models

arXiv 2508.00726 ↗First posted 1 Aug 2025cs.CVcited by 2

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

Abstract (arXiv)

Despite growing interest in hallucination in Multimodal Large Language Models, existing studies primarily focus on single-image settings, leaving hallucination in multi-image scenarios largely unexplored. To address this gap, we conduct the first systematic study of hallucinations in multi-image MLLMs and propose MIHBench, a benchmark specifically tailored for evaluating object-related hallucinations across multiple images. MIHBench comprises three core tasks: Multi-Image Object Existence Hallucination, Multi-Image Object Count Hallucination, and Object Identity Consistency Hallucination, targeting semantic understanding across object existence, quantity reasoning, and cross-view identity consistency. Through extensive evaluation, we identify key factors associated with the occurrence of multi-image hallucinations, including: a progressive relationship between the number of image inputs and the likelihood of hallucination occurrences; a strong correlation between single-image hallucination tendencies and those observed in multi-image contexts; and the influence of same-object image ratios and the positional placement of negative samples within image sequences on the occurrence of object identity consistency hallucination. To address these challenges, we propose a Dynamic Attention Balancing mechanism that adjusts inter-image attention distributions while preserving the overall visual attention proportion. Experiments across multiple state-of-the-art MLLMs demonstrate that our method effectively reduces hallucination occurrences and enhances semantic integration and reasoning stability in multi-image scenarios.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Hallucination mitigation in LVLMs, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 34 of its 54 references that are in the map. First posted on arXiv 1 Aug 2025.

Builds on

34 of 54 references.

Closest ancestors · 11

Same direction, newest first

  1. 2024Mitigating Object Hallucination via Concentric Causal Attention21 Oct
  2. 2024EventHallusion: Diagnosing Event Hallucinations in Video LLMs25 Sep
  3. 2024Paying More Attention to Image: A Training-Free Method for Alleviating Hallucination in LVLMs31 Jul
  4. 2024Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding27 Mar
  5. 2024IBD: Alleviating Hallucinations in Large Vision-Language Models via Image-Biased Decoding28 Feb
  6. 2023OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocation29 Nov
  7. 2023Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding28 Nov
  8. 2023HalluciDoctor: Mitigating Hallucinatory Toxicity in Visual Instruction Data22 Nov
  9. 2023Woodpecker: Hallucination Correction for Multimodal Large Language Models24 Oct
  10. 2023Analyzing and Mitigating Object Hallucination in Large Vision-Language Models1 Oct
  11. 2023Evaluating Object Hallucination in Large Vision-Language Models17 May

Other references · 23

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  3. 2023InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuningcited by 2,876
  4. 2024Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scalingcited by 1,599
  5. 2023LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Daycited by 1,305
  6. 2024ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Toolscited by 1,121
  7. 2023Kosmos-2: Grounding Multimodal Large Language Models to the Worldcited by 1,089
  8. 2023Shikra: Unleashing Multimodal LLM's Referential Dialogue Magiccited by 906
  9. 2024DeepSeek-VL: Towards Real-World Vision-Language Understandingcited by 733
  10. 2024MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategiescited by 723
  11. 2021Multimodal Few-Shot Learning with Frozen Language Modelscited by 661
  12. 2021Common Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstructioncited by 652
11 more
  1. 2023mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaborationcited by 547
  2. 2024InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Modelcited by 326
  3. 2023OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documentscited by 316
  4. 2024MANTIS: Interleaved Multi-Image Instruction Tuningcited by 216
  5. 2024MuirBench: A Comprehensive Benchmark for Robust Multi-image Understandingcited by 188
  6. 2024LongLLaVA: Scaling Multi-modal LLMs to 1000 Images Efficiently via a Hybrid Architecturecited by 102
  7. 2024MileBench: Benchmarking MLLMs in Long Contextcited by 88
  8. 2024Towards Open-ended Visual Quality Comparisoncited by 85
  9. 2024Benchmarking Multi-Image Understanding in Vision and Language Models: Perception, Knowledge, Reasoning, and Multi-Hop Reasoningcited by 54
  10. 2024TAR3D: Creating High-Quality 3D Assets via Next-Part Predictioncited by 16
  11. 2025AR-1-to-3: Single Image to Consistent 3D Object Generation via Next-View Predictioncited by 15

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Bi-Level Prompt Optimization for Multimodal LLM-as-a-Judgecited by 1
  2. 2026Fine-Grained Multi Image Object Hallucination Benchmarkcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Multi-Object Hallucination in Vision-Language Models
  2. 2025A Survey of Multimodal Hallucination Evaluation and Detection
  3. 2025Evaluating Hallucination in Large Vision-Language Models based on Context-Aware Object Similarities
  4. 2025Mitigating Behavioral Hallucination in Multimodal Large Language Models for Sequential Images
  5. 2024Hallu-PI: Evaluating Hallucination in Multi-modal Large Language Models within Perturbed Inputs
  6. 2026ReactBench: A Cause-Driven Benchmark for Multimodal Hallucination via Systematic Evaluation
  7. 2024ODE: Open-Set Evaluation of Hallucinations in Multimodal Large Language Models
  8. 2024Hallucination in Multimodal Foundation Models: A Survey on Causes, Corrections, and Evaluations