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From Generation to Detection: Exploration of Discourse Driven Scenario based LLM Generated Fake News

arXiv 2609.20838 ↗First posted 29 Jul 2026cs.CLcited by 0

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Abstract (arXiv)

In this study, we examine how modern LLMs generate and detect fake news under controlled settings across four manipulation scenarios. These are open-ended generation, rewriting, manipulation prompts and attribute based prompts grounded in the journalistic discourse framework. Firstly, using seven widely adapted models, we created a synthetic fake news corpus with 14000 generated articles across these four scenarios. Then we analyzed its linguistic properties to assess how closely model-generated news resembles real news structurally and semantically. Finally, to evaluate detection performance, we conducted experiments where each model judges generated fake news, starting with a basic detection prompt and improved prompts developed through an iterative refinement process that extracts misleading patterns from real-fake pairs. Our results revealed substantial variation across models in both generating and detecting misinformation, demonstrated that the generation strategy strongly influences detectability, and show that the refined prompt does not improve and often harms detection performance. Therefore, the study provides a systematic assessment of LLMs detection capability of LLMs generated fake news across typical generation scenarios.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 37 references that are in the map. First posted on arXiv 29 Jul 2026.

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References · 23

  1. 2024The Llama 3 Herd of Modelscited by 14,715
  2. 2025Qwen3 Technical Reportcited by 8,820
  3. 2025Gemma 3 Technical Reportcited by 1,824
  4. 2016Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyondcited by 812
  5. 2024Phi-4 Technical Reportcited by 711
  6. 2023Can LLM-Generated Misinformation Be Detected?cited by 177
  7. 2017Automatic Detection of Fake Newscited by 145
  8. 2023Towards LLM-based Fact Verification on News Claims with a Hierarchical Step-by-Step Prompting Methodcited by 97
  9. 2023Combating Misinformation in the Age of LLMs: Opportunities and Challengescited by 95
  10. 2023Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News Detectioncited by 72
  11. 2024A Complete Survey on LLM-based AI Chatbotscited by 71
  12. 2023Fake News in Sheep's Clothing: Robust Fake News Detection Against LLM-Empowered Style Attackscited by 70
11 more
  1. 2024A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Modelscited by 52
  2. 2023Fighting Fire with Fire: The Dual Role of LLMs in Crafting and Detecting Elusive Disinformationcited by 51
  3. 2023Disinformation Detection: An Evolving Challenge in the Age of LLMscited by 41
  4. 2023News Verifiers Showdown: A Comparative Performance Evaluation of ChatGPT 3.5, ChatGPT 4.0, Bing AI, and Bard in News Fact-Checkingcited by 16
  5. 2023Explore the Potential of LLMs in Misinformation Detection: An Empirical Studycited by 12
  6. 2024From Deception to Detection: The Dual Roles of Large Language Models in Fake Newscited by 11
  7. 2024Comparing GPT-4 and Open-Source Language Models in Misinformation Mitigationcited by 9
  8. 2024Can Large Language Models Detect Misinformation in Scientific News Reporting?cited by 9
  9. 2025Exploring Jailbreak Attacks on LLMs through Intent Concealment and Diversioncited by 9
  10. 2025Battling Misinformation: An Empirical Study on Adversarial Factuality in Open-Source Large Language Modelscited by 6
  11. 2024Catching Chameleons: Detecting Evolving Disinformation Generated using Large Language Modelscited by 5

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