mlsanityData to 7 Oct 2026

Multimodal Large Language Models to Support Real-World Fact-Checking

arXiv 2403.03627 ↗First posted 6 Mar 2024cs.CL cs.AIcited by 13

On the map: Multimodal LLMs / vision-language models → MLLM jailbreaks and adversarial robustness

Abstract (arXiv)

Multimodal large language models (MLLMs) carry the potential to support humans in processing vast amounts of information. While MLLMs are already being used as a fact-checking tool, their abilities and limitations in this regard are understudied. Here is aim to bridge this gap. In particular, we propose a framework for systematically assessing the capacity of current multimodal models to facilitate real-world fact-checking. Our methodology is evidence-free, leveraging only these models' intrinsic knowledge and reasoning capabilities. By designing prompts that extract models' predictions, explanations, and confidence levels, we delve into research questions concerning model accuracy, robustness, and reasons for failure. We empirically find that (1) GPT-4V exhibits superior performance in identifying malicious and misleading multimodal claims, with the ability to explain the unreasonable aspects and underlying motives, and (2) existing open-source models exhibit strong biases and are highly sensitive to the prompt. Our study offers insights into combating false multimodal information and building secure, trustworthy multimodal models. To the best of our knowledge, we are the first to evaluate MLLMs for real-world fact-checking.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Placed in MLLM jailbreaks and adversarial robustness, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 22 of its 33 references that are in the map. First posted on arXiv 6 Mar 2024.

Builds on

22 of 33 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024SNIFFER: Multimodal Large Language Model for Explainable Out-of-Context Misinformation Detection5 Mar

Other references · 21

  1. 2017On Calibration of Modern Neural Networkscited by 4,860
  2. 2023Improved Baselines with Visual Instruction Tuningcited by 4,362
  3. 2023InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuningcited by 2,855
  4. 2023MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Modelscited by 1,624
  5. 2023Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understandingcited by 1,461
  6. 2023FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generationcited by 1,259
  7. 2023Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMscited by 946
  8. 2023Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificitycited by 204
  9. 2023Fact-Checking Complex Claims with Program-Guided Reasoningcited by 146
  10. 2021Automated Fact-Checking for Assisting Human Fact-Checkerscited by 144
  11. 2018Fighting Fake News: Image Splice Detection via Learned Self-Consistencycited by 136
  12. 2021NewsCLIPpings: Automatic Generation of Out-of-Context Multimodal Mediacited by 106
9 more
  1. 2022End-to-End Multimodal Fact-Checking and Explanation Generation: A Challenging Dataset and Modelscited by 105
  2. 2023How Many Unicorns Are in This Image? A Safety Evaluation Benchmark for Vision LLMscited by 103
  3. 2019Fact-Checking Meets Fauxtography: Verifying Claims About Imagescited by 52
  4. 2023Multimodal Automated Fact-Checking: A Surveycited by 51
  5. 2023Do Large Language Models Know about Facts?cited by 49
  6. 2022GERE: Generative Evidence Retrieval for Fact Verificationcited by 38
  7. 2023FactLLaMA: Optimizing Instruction-Following Language Models with External Knowledge for Automated Fact-Checkingcited by 37
  8. 2023Read it Twice: Towards Faithfully Interpretable Fact Verification by Revisiting Evidencecited by 17
  9. 2023News Verifiers Showdown: A Comparative Performance Evaluation of ChatGPT 3.5, ChatGPT 4.0, Bing AI, and Bard in News Fact-Checkingcited by 16

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 5; 2025: 7; 2026: 1.

13 papers.

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  1. 2024DEFAME: Dynamic Evidence-based FAct-checking with Multimodal Expertscited by 42
  2. 2024Reference-free Hallucination Detection for Large Vision-Language Modelscited by 23
  3. 2025An Empirical Analysis of Uncertainty in Large Language Model Evaluationscited by 20
  4. 2024Can LLMs Improve Multimodal Fact-Checking by Asking Relevant Questions?cited by 17
  5. 2025Hallucination to Truth: A Review of Fact-Checking and Factuality Evaluation in Large Language Modelscited by 13
  6. 2024Piecing It All Together: Verifying Multi-Hop Multimodal Claimscited by 12
  7. 2024Multimodal Fact-Checking with Vision Language Models: A Probing Classifier based Solution with Embedding Strategiescited by 10
  8. 2025M4FC: a Multimodal, Multilingual, Multicultural, Multitask Real-World Fact-Checking Datasetcited by 7
  9. 2025XFacta: Contemporary, Real-World Dataset and Evaluation for Multimodal Misinformation Detection with Multimodal LLMscited by 6
  10. 2025Drifting Away from Truth: GenAI-Driven News Diversity Challenges LVLM-Based Misinformation Detectioncited by 6
  11. 2025HintsOfTruth: A Multimodal Checkworthiness Detection Dataset with Real and Synthetic Claimscited by 4
  12. 2025Understanding Inequality of LLM Fact-Checking over Geographic Regions with Agent and Retrieval modelscited by 0
1 more
  1. 2026Multilingual, Multimodal Pipeline for Creating Authentic and Structured Fact-Checked Claim Datasetcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025RealFactBench: A Benchmark for Evaluating Large Language Models in Real-World Fact-Checking
  2. 2024MultiTrust: A Comprehensive Benchmark Towards Trustworthy Multimodal Large Language Models
  3. 2025Hidden in Plain Sight: Reasoning in Underspecified and Misspecified Scenarios for Multimodal LLMs
  4. 2024MFC-Bench: Benchmarking Multimodal Fact-Checking with Large Vision-Language Models
  5. 2024Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning
  6. 2025Beyond Retrieval: Improving Evidence Quality for LLM-based Multimodal Fact-Checking
  7. 2026Multimodal Fact-Level Attribution for Verifiable Reasoning
  8. 2024MLLMGuard: A Multi-dimensional Safety Evaluation Suite for Multimodal Large Language Models