mlsanityData to 14 Oct 2026

Disinformation Capabilities of Large Language Models

arXiv 2311.08838 ↗First posted 15 Nov 2023cs.CLcited by 25

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Abstract (arXiv)

Automated disinformation generation is often listed as an important risk associated with large language models (LLMs). The theoretical ability to flood the information space with disinformation content might have dramatic consequences for societies around the world. This paper presents a comprehensive study of the disinformation capabilities of the current generation of LLMs to generate false news articles in the English language. In our study, we evaluated the capabilities of 10 LLMs using 20 disinformation narratives. We evaluated several aspects of the LLMs: how good they are at generating news articles, how strongly they tend to agree or disagree with the disinformation narratives, how often they generate safety warnings, etc. We also evaluated the abilities of detection models to detect these articles as LLM-generated. We conclude that LLMs are able to generate convincing news articles that agree with dangerous disinformation narratives.

Key facts. Cited by 25 papers in the map (data through 14 Oct 2026). Builds on 14 of its 44 references that are in the map. First posted on arXiv 15 Nov 2023.

Builds on

14 of 44 references.

References · 14

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2019Defending Against Neural Fake Newscited by 559
  3. 2019Release Strategies and the Social Impacts of Language Modelscited by 513
  4. 2019GLTR: Statistical Detection and Visualization of Generated Textcited by 496
  5. 2023How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detectioncited by 481
  6. 2023Low-Resource Languages Jailbreak GPT-4cited by 309
  7. 2023How is ChatGPT's behavior changing over time?cited by 270
  8. 2022OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalizationcited by 236
  9. 2023Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMscited by 184
  10. 2023Generative Language Models and Automated Influence Operations: Emerging Threats and Potential Mitigationscited by 158
  11. 2021TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generationcited by 120
  12. 2020TweepFake: about Detecting Deepfake Tweetscited by 117
2 more
  1. 2019Generating Sentiment-Preserving Fake Online Reviews Using Neural Language Models and Their Human- and Machine-based Detectioncited by 52
  2. 2023Reliability Check: An Analysis of GPT-3's Response to Sensitive Topics and Prompt Wordingcited by 13

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 4; 2025: 8; 2026: 13.

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  1. 2024Source Code Summarization in the Era of Large Language Modelscited by 31
  2. 2024Exploring the Deceptive Power of LLM-Generated Fake News: A Study of Real-World Detection Challengescited by 30
  3. 2025Token Buncher: Shielding LLMs from Harmful Reinforcement Learning Fine-Tuningcited by 8
  4. 2024MegaFake: A Theory-Driven Dataset of Fake News Generated by Large Language Modelscited by 7
  5. 2025FakeSV-VLM: Taming VLM for Detecting Fake Short-Video News via Progressive Mixture-Of-Experts Adaptercited by 7
  6. 2026PASA: A Principled Embedding-Space Watermarking Approach for LLM-Generated Text under Semantic-Invariant Attackscited by 7
  7. 2025mdok of KInIT: Robustly Fine-tuned LLM for Binary and Multiclass AI-Generated Text Detectioncited by 6
  8. 2024A Survey on Automatic Credibility Assessment Using Textual Credibility Signals in the Era of Large Language Modelscited by 3
  9. 2025Information Retrieval in the Age of Generative AI: The RGB Modelcited by 2
  10. 2025Tailored untruths: How personalisation challenges LLM safeguardscited by 2
  11. 2026Beyond Raw Detection Scores: Markov-Informed Calibration for Boosting Machine-Generated Text Detectioncited by 2
  12. 2026BLUFF: Benchmarking the Detection of False and Synthetic Content across 58 Low-Resource Languagescited by 2
13 more
  1. 2026Retrieving Climate Change Disinformation by Narrativecited by 2
  2. 2025EAMET: Robust Massive Model Editing via Embedding Alignment Optimizationcited by 1
  3. 2026Jailbreaking LLMs Without Gradients or Priors: Effective and Transferable Attackscited by 1
  4. 2025A Peek Behind the Curtain: Using Step-Around Prompt Engineering to Identify Bias and Misinformation in GenAI Modelscited by 0
  5. 2025WaterSearch: Exploring Seed Pooling for Improving the Quality-Detectability Trade-off in LLM Watermarkingcited by 0
  6. 2026Ignore All Previous Instructions: Jailbreaking as a de-escalatory peace building practise to resist LLM social media botscited by 0
  7. 2026From Shallow to Deep: Pinning Semantic Intent via Causal GRPOcited by 0
  8. 2026LLMs Should Incorporate Explicit Mechanisms for Human Empathycited by 0
  9. 2026Multi-Level Contextual Token Relation Modeling for Machine-Generated Text Detectioncited by 0
  10. 2026Using LLMs to Detect LLM-Generated Texts: A Cross-Generation Analysiscited by 0
  11. 2026VEX-Bench: Benchmarking Verification Complexity of LLM-Generated Misinformationcited by 0
  12. 2026CrisisFake: Benchmark Validity of AI-Generated Text Detection for Disaster Social Sensingcited by 0
  13. 2026Misinformation Without Triggers: From Factual Answers to Downstream Decisionscited by 0