The Earth is Flat because...: Investigating LLMs' Belief towards Misinformation via Persuasive Conversation
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Abstract (arXiv)
Large language models (LLMs) encapsulate vast amounts of knowledge but still remain vulnerable to external misinformation. Existing research mainly studied this susceptibility behavior in a single-turn setting. However, belief can change during a multi-turn conversation, especially a persuasive one. Therefore, in this study, we delve into LLMs' susceptibility to persuasive conversations, particularly on factual questions that they can answer correctly. We first curate the Farm (i.e., Fact to Misinform) dataset, which contains factual questions paired with systematically generated persuasive misinformation. Then, we develop a testing framework to track LLMs' belief changes in a persuasive dialogue. Through extensive experiments, we find that LLMs' correct beliefs on factual knowledge can be easily manipulated by various persuasive strategies.
Key facts. Cited by 109 papers in the map (data through 14 Oct 2026). Builds on 41 of its 97 references that are in the map. First posted on arXiv 14 Dec 2023.
Builds on
41 of 97 references.
References · 41
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Citing papers per year: 2024: 29; 2025: 44; 2026: 36.
109 papers.
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- 2026Caught in the Story: Narrative Captivity in Multi-turn LLMs Conversation3 Sep 2026
- 2026Untangling the Mechanisms of Misleading Context in Medical Question Answering2 Sep 2026
- 2026Belief Cascades Drive Persuasion in LLM Agent Networks25 Aug 2026
- 2026Affective Context Amplifies Sycophancy in LLM Responses21 Aug 2026
- 2026Do SpeechLMs Hear Their Own Opinions? Diagnosing and Mitigating Previous-Belief Contamination in Streaming Emotion Understanding21 Aug 2026
- 2026Jagged Judges: Epistemic Stability Under Perturbation, Pressure, and Persistence12 Aug 2026
- 2026Build it, Break it, Repeat: Benchmarking and improving LLM-manipulated disinformation detection in social media posts10 Aug 2026
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- 2026Tackling the Root of Misinformation by Teaching Laypeople about Logical Fallacies via Socratic Questioning and Critical Argumentation31 May 2026
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- 2024A Survey on Large Language Model-Based Social Agents in Game-Theoretic Scenarioscited by 23
- 2025When Persuasion Overrides Truth in Multi-Agent LLM Debates: Introducing a Confidence-Weighted Persuasion Override Rate (CW-POR)cited by 12
- 2025Helpful Agent Meets Deceptive Judge: Understanding Vulnerabilities in Agentic Workflowscited by 7
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- 2026Misinformation Propagation in Benign Multi-Agent Systemscited by 2
- 2026AILS-NTUA at SemEval-2026 Task 10: Agentic LLMs for Psycholinguistic Marker Extraction and Conspiracy Endorsement Detectioncited by 0
- 2026Belief Cascades Drive Persuasion in LLM Agent Networkscited by 0
- 2026What Does Multi-Agent LLM Debate Actually Change? A Layered Analysis of Disagreement and Answer Qualitycited by 0
- 2026Do LLMs Trust the Accuser or the Accusation? Measuring Belief Shifts in Werewolfcited by 0
- 2026Silent Dissent: LLM Agents That Yield to the Majority Still Represent Their Original Premisecited by 0
- 2026Silent Dissent: LLM Agents That Yield to the Majority Still Represent Their Original Premise2 Oct 2026
- 2026Do LLMs Trust the Accuser or the Accusation? Measuring Belief Shifts in Werewolf11 Sep 2026
- 2026What Does Multi-Agent LLM Debate Actually Change? A Layered Analysis of Disagreement and Answer Quality7 Sep 2026
- 2026Belief Cascades Drive Persuasion in LLM Agent Networks25 Aug 2026
- 2026Misinformation Propagation in Benign Multi-Agent Systems15 Jun 2026
- 2026AILS-NTUA at SemEval-2026 Task 10: Agentic LLMs for Psycholinguistic Marker Extraction and Conspiracy Endorsement Detection5 Mar 2026
- 2026When Agents "Misremember" Collectively: Exploring the Mandela Effect in LLM-based Multi-Agent Systems31 Jan 2026
- 2025Stop Reducing Responsibility in LLM-Powered Multi-Agent Systems to Local Alignment15 Oct 2025
- 2025Helpful Agent Meets Deceptive Judge: Understanding Vulnerabilities in Agentic Workflows3 Jun 2025
- 2025When Persuasion Overrides Truth in Multi-Agent LLM Debates: Introducing a Confidence-Weighted Persuasion Override Rate (CW-POR)1 Apr 2025
- 2024A Survey on Large Language Model-Based Social Agents in Game-Theoretic Scenarios5 Dec 2024
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- 2026Do Images Speak Louder than Words? Investigating the Effect of Textual Misinformation in VLMscited by 7
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- 2026Lying with Truths: Open-Channel Multi-Agent Collusion for Belief Manipulation via Generative Montage4 Jan 2026
- 2025Nuclear Deployed: Analyzing Catastrophic Risks in Decision-making of Autonomous LLM Agents17 Feb 2025
- 2024Understanding the Dark Side of LLMs' Intrinsic Self-Correction19 Dec 2024
- 2024Preemptive Answer "Attacks" on Chain-of-Thought Reasoning31 May 2024
- 2024How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMs12 Jan 2024
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- 2025Exploring the Impact of Instruction-Tuning on LLM's Susceptibility to Misinformation24 Jul 2025
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- 2026Mechanisms of Prompt-Induced Hallucination in Vision-Language Models8 Jan 2026
- 2025Seeing is Believing? Evaluating Vision-Language Model Susceptibility in Agent-to-Agent Multimodal Persuasion26 Oct 2025
- 2025Phi: Preference Hijacking in Multi-modal Large Language Models at Inference Time15 Sep 2025
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- 2024FB-Bench: A Fine-Grained Multi-Task Benchmark for Evaluating LLMs' Responsiveness to Human Feedback12 Oct 2024
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- 2024Know Your Limits: A Survey of Abstention in Large Language Modelscited by 121
- 2024DELL: Generating Reactions and Explanations for LLM-Based Misinformation Detectioncited by 41
- 2024Knowledge Boundary of Large Language Models: A Surveycited by 40
- 2024DYNAMICQA: Tracing Internal Knowledge Conflicts in Language Modelscited by 34
- 2024Navigating LLM Ethics: Advancements, Challenges, and Future Directionscited by 28
- 2025Trust Me, I'm Wrong: LLMs Hallucinate with Certainty Despite Knowing the Answercited by 27
- 2025Persuade Me if You Can: A Framework for Evaluating Persuasion Effectiveness and Susceptibility Among Large Language Modelscited by 27
- 2024What Does the Bot Say? Opportunities and Risks of Large Language Models in Social Media Bot Detectioncited by 25
- 2024"They are uncultured": Unveiling Covert Harms and Social Threats in LLM Generated Conversationscited by 25
- 2024Fundamental Problems With Model Editing: How Should Rational Belief Revision Work in LLMs?cited by 24
- 2025LLM Can be a Dangerous Persuader: Empirical Study of Persuasion Safety in Large Language Modelscited by 23
12 most cited of 70 All 70 (JSON)
- 2026Not All Answers Are Contextually Persuadable: Inference Dynamics in Large Language Models under Contextual Influence3 Oct 2026
- 2026VEX-Bench: Benchmarking Verification Complexity of LLM-Generated Misinformation28 Sep 2026
- 2026Caught in the Story: Narrative Captivity in Multi-turn LLMs Conversation3 Sep 2026
- 2026Affective Context Amplifies Sycophancy in LLM Responses21 Aug 2026
- 2026Do SpeechLMs Hear Their Own Opinions? Diagnosing and Mitigating Previous-Belief Contamination in Streaming Emotion Understanding21 Aug 2026
- 2026Build it, Break it, Repeat: Benchmarking and improving LLM-manipulated disinformation detection in social media posts10 Aug 2026
- 2026It's Not What You Say, It's How You Say It: Evaluating LLM Responses to Expressions of Belief20 Jul 2026
- 2026Navigating Unreliable Parametric and Contextual Knowledge: Explicit Knowledge Conflict Resolution for LLM Inference18 Jun 2026
- 2026Right or Wrong, Models Comply: Directional Blindness in LLM Moral Judgment12 Jun 2026
- 2026Tackling the Root of Misinformation by Teaching Laypeople about Logical Fallacies via Socratic Questioning and Critical Argumentation31 May 2026
- 2026When Should Models Change Their Minds? Contextual Belief Management in Large Language Models28 May 2026
- 2026How LLMs Are Persuaded: A Few Attention Heads, Rerouted10 May 2026
12 newest of 70 All 70 (JSON)