mlsanityData to 7 Oct 2026

Disinformation Detection: An Evolving Challenge in the Age of LLMs

arXiv 2309.15847 ↗First posted 25 Sep 2023cs.CL cs.AI cs.CYcited by 41

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Abstract (arXiv)

The advent of generative Large Language Models (LLMs) such as ChatGPT has catalyzed transformative advancements across multiple domains. However, alongside these advancements, they have also introduced potential threats. One critical concern is the misuse of LLMs by disinformation spreaders, leveraging these models to generate highly persuasive yet misleading content that challenges the disinformation detection system. This work aims to address this issue by answering three research questions: (1) To what extent can the current disinformation detection technique reliably detect LLM-generated disinformation? (2) If traditional techniques prove less effective, can LLMs themself be exploited to serve as a robust defense against advanced disinformation? and, (3) Should both these strategies falter, what novel approaches can be proposed to counter this burgeoning threat effectively? A holistic exploration for the formation and detection of disinformation is conducted to foster this line of research.

Key facts. Cited by 41 papers in the map (data through 7 Oct 2026). Builds on 24 of its 35 references that are in the map. First posted on arXiv 25 Sep 2023.

Builds on

24 of 35 references.

References · 24

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12 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 23; 2025: 12; 2026: 4.

41 papers.

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  1. 2024Knowledge Conflicts for LLMs: A Surveycited by 274
  2. 2023The Earth is Flat because...: Investigating LLMs' Belief towards Misinformation via Persuasive Conversationcited by 107
  3. 2024DELL: Generating Reactions and Explanations for LLM-Based Misinformation Detectioncited by 41
  4. 2024Exploring the Deceptive Power of LLM-Generated Fake News: A Study of Real-World Detection Challengescited by 28
  5. 2024Navigating LLM Ethics: Advancements, Challenges, and Future Directionscited by 28
  6. 2024What Does the Bot Say? Opportunities and Risks of Large Language Models in Social Media Bot Detectioncited by 24
  7. 2024Detecting AI-Generated Text: Factors Influencing Detectability with Current Methodscited by 23
  8. 2024Generative Large Language Models in Automated Fact-Checking: A Surveycited by 19
  9. 2025LLM-Generated Fake News Induces Truth Decay in News Ecosystem: A Case Study on Neural News Recommendationcited by 19
  10. 2023User Modeling in the Era of Large Language Models: Current Research and Future Directionscited by 18
  11. 2024A Survey of AI-generated Text Forensic Systems: Detection, Attribution, and Characterizationcited by 18
  12. 2024Can LLMs Improve Multimodal Fact-Checking by Asking Relevant Questions?cited by 17
29 more
  1. 2025How to Protect Yourself from 5G Radiation? Investigating LLM Responses to Implicit Misinformationcited by 17
  2. 2024Let Silence Speak: Enhancing Fake News Detection with Generated Comments from Large Language Modelscited by 16
  3. 2024Zero-Shot Detection of LLM-Generated Text using Token Cohesivenesscited by 13
  4. 2025Firm or Fickle? Evaluating Large Language Models Consistency in Sequential Interactionscited by 12
  5. 2024Prospects for inconsistency detection using large language models and sheavescited by 10
  6. 2024Online Learning of Multiple Tasks and Their Relationships : Testing on Spam Email Data and EEG Signals Recorded in Construction Fieldscited by 8
  7. 2025VLDBench Evaluating Multimodal Disinformation with Regulatory Alignmentcited by 8
  8. 2024Fake Artificial Intelligence Generated Contents (FAIGC): A Survey of Theories, Detection Methods, and Opportunitiescited by 7
  9. 2024Have LLMs Reopened the Pandora's Box of AI-Generated Fake News?cited by 6
  10. 2024Catching Chameleons: Detecting Evolving Disinformation Generated using Large Language Modelscited by 5
  11. 2024MegaFake: A Theory-Driven Dataset of Fake News Generated by Large Language Modelscited by 5
  12. 2024Seeing Through AI's Lens: Enhancing Human Skepticism Towards LLM-Generated Fake Newscited by 4
  13. 2024LLM-GAN: Construct Generative Adversarial Network Through Large Language Models For Explainable Fake News Detectioncited by 4
  14. 2025Fake News Detection After LLM Laundering: Measurement and Explanationcited by 4
  15. 2025An Explainable Framework for Misinformation Identification via Critical Question Answeringcited by 4
  16. 2025Prompt-Induced Linguistic Fingerprints for LLM-Generated Fake News Detectioncited by 3
  17. 2024Model Attribution in LLM-Generated Disinformation: A Domain Generalization Approach with Supervised Contrastive Learningcited by 2
  18. 2024Assessing the Impact of Conspiracy Theories Using Large Language Modelscited by 2
  19. 2025From Misleading Queries to Accurate Answers: A Three-Stage Fine-Tuning Method for LLMscited by 2
  20. 2024Real-Time Summarization of Twittercited by 1
  21. 2025Decoding News Bias: Multi Bias Detection in News Articlescited by 1
  22. 2025Are LLMs Enough for Hyperpartisan, Fake, Polarized and Harmful Content Detection? Evaluating In-Context Learning vs. Fine-Tuningcited by 1
  23. 2025Effectiveness of Large Multimodal Models in Detecting Disinformation: Experimental Resultscited by 1
  24. 2024Using GPT Models for Qualitative and Quantitative News Analytics in the 2024 US Presidental Election Processcited by 0
  25. 2025CrediBench: Building Web-Scale Network Datasets for Information Integritycited by 0
  26. 2026D-SECURE: Dual-Source Evidence Combination for Unified Reasoning in Misinformation Detectioncited by 0
  27. 2026Ignore All Previous Instructions: Jailbreaking as a de-escalatory peace building practise to resist LLM social media botscited by 0
  28. 2026Unequal Verdicts: Investigating Gender Bias in LLM-Based Fake News Detectioncited by 0
  29. 2026From Generation to Detection: Exploration of Discourse Driven Scenario based LLM Generated Fake Newscited by 0