# Persuasion with Large Language Models: A Survey of Empirical Evidence, Study Methodologies, and Ethical Implications

arXiv: https://arxiv.org/abs/2411.06837 · HTML: https://mlsanity.com/paper/2411.06837/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 98 references that are in the map.
- First posted on arXiv 11 Nov 2024.

## Builds on (most cited first, 24 of 24)

- [An Overview of Catastrophic AI Risks](https://mlsanity.com/paper/2306.12001/) (2023)
- [Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good](https://mlsanity.com/paper/1906.06725/) (2019)
- [Combating Misinformation in the Age of LLMs: Opportunities and Challenges](https://mlsanity.com/paper/2311.05656/) (2023)
- [AI model GPT-3 (dis)informs us better than humans](https://mlsanity.com/paper/2301.11924/) (2023)
- [Hidden Persuaders: LLMs' Political Leaning and Their Influence on Voters](https://mlsanity.com/paper/2410.24190/) (2024)
- [Large Language Models Reflect the Ideology of their Creators](https://mlsanity.com/paper/2410.18417/) (2024)
- [The Persuasive Power of Large Language Models](https://mlsanity.com/paper/2312.15523/) (2023)
- [Characterizing Manipulation from AI Systems](https://mlsanity.com/paper/2303.09387/) (2023)
- [Measuring and Benchmarking Large Language Models' Capabilities to Generate Persuasive Language](https://mlsanity.com/paper/2406.17753/) (2024)
- [Quantifying the Impact of Large Language Models on Collective Opinion Dynamics](https://mlsanity.com/paper/2308.03313/) (2023)
- [Susceptibility to Influence of Large Language Models](https://mlsanity.com/paper/2303.06074/) (2023)
- [MindShift: Leveraging Large Language Models for Mental-States-Based Problematic Smartphone Use Intervention](https://mlsanity.com/paper/2309.16639/) (2023)
- [Objection Overruled! Lay People can Distinguish Large Language Models from Lawyers, but still Favour Advice from an LLM](https://mlsanity.com/paper/2409.07871/) (2024)
- [Artificial Intelligence for Health Message Generation: Theory, Method, and an Empirical Study Using Prompt Engineering](https://mlsanity.com/paper/2212.07507/) (2022)
- [Zero-shot Persuasive Chatbots with LLM-Generated Strategies and Information Retrieval](https://mlsanity.com/paper/2407.03585/) (2024)
- [Evidence of a log scaling law for political persuasion with large language models](https://mlsanity.com/paper/2406.14508/) (2024)
- [Resistance Against Manipulative AI: key factors and possible actions](https://mlsanity.com/paper/2404.14230/) (2024)
- [The effect of source disclosure on evaluation of AI-generated messages: A two-part study](https://mlsanity.com/paper/2311.15544/) (2023)
- [The Adoption and Efficacy of Large Language Models: Evidence From Consumer Complaints in the Financial Industry](https://mlsanity.com/paper/2311.16466/) (2023)
- [Persuasiveness of Generated Free-Text Rationales in Subjective Decisions: A Case Study on Pairwise Argument Ranking](https://mlsanity.com/paper/2406.13905/) (2024)
- [Beyond Persuasion: Towards Conversational Recommender System with Credible Explanations](https://mlsanity.com/paper/2409.14399/) (2024)
- [AutoPersuade: A Framework for Evaluating and Explaining Persuasive Arguments](https://mlsanity.com/paper/2410.08917/) (2024)
- [Large Language Model Agents for Improving Engagement with Behavior Change Interventions: Application to Digital Mindfulness](https://mlsanity.com/paper/2407.13067/) (2024)
- [Persuasion Games using Large Language Models](https://mlsanity.com/paper/2408.15879/) (2024)

## Built on by (most cited first, 30 of 32)

- [LLM Can be a Dangerous Persuader: Empirical Study of Persuasion Safety in Large Language Models](https://mlsanity.com/paper/2504.10430/) (2025)
- [The Levers of Political Persuasion with Conversational AI](https://mlsanity.com/paper/2507.13919/) (2025)
- [Lies, Damned Lies, and Distributional Language Statistics: Persuasion and Deception with Large Language Models](https://mlsanity.com/paper/2412.17128/) (2024)
- [It's the Thought that Counts: Evaluating the Attempts of Frontier LLMs to Persuade on Harmful Topics](https://mlsanity.com/paper/2506.02873/) (2025)
- [Must Read: A Comprehensive Survey of Computational Persuasion](https://mlsanity.com/paper/2505.07775/) (2025)
- [Persuasion Dynamics in LLMs: Investigating Robustness and Adaptability in Knowledge and Safety with DuET-PD](https://mlsanity.com/paper/2508.17450/) (2025)
- [ToMAP: Training Opponent-Aware LLM Persuaders with Theory of Mind](https://mlsanity.com/paper/2505.22961/) (2025)
- [Do Large Language Models Have a Planning Theory of Mind? Evidence from MindGames: a Multi-Step Persuasion Task](https://mlsanity.com/paper/2507.16196/) (2025)
- [Among Them: A game-based framework for assessing persuasion capabilities of LLMs](https://mlsanity.com/paper/2502.20426/) (2025)
- [Persuasion Should be Double-Blind: A Multi-Domain Dialogue Dataset With Faithfulness Based on Causal Theory of Mind](https://mlsanity.com/paper/2502.21297/) (2025)
- [Beyond One-Way Influence: Bidirectional Opinion Dynamics in Multi-Turn Human-LLM Interactions](https://mlsanity.com/paper/2510.20039/) (2025)
- [Can (A)I Change Your Mind?](https://mlsanity.com/paper/2503.01844/) (2025)
- [OpenReview Should be Protected and Leveraged as a Community Asset for Research in the Era of Large Language Models](https://mlsanity.com/paper/2505.21537/) (2025)
- [Ads in AI Chatbots? An Analysis of How Large Language Models Navigate Conflicts of Interest](https://mlsanity.com/paper/2604.08525/) (2026)
- [LLMs can persuade only psychologically susceptible humans on societal issues, via trust in AI and emotional appeals, amid logical fallacies](https://mlsanity.com/paper/2604.16935/) (2026)
- [Measuring Epistemic Resilience of LLMs Under Misleading Medical Context](https://mlsanity.com/paper/2606.12291/) (2026)
- [PRISON: Unmasking the Criminal Potential of Large Language Models](https://mlsanity.com/paper/2506.16150/) (2025)
- [The Bots of Persuasion: Examining How Conversational Agents' Linguistic Expressions of Personality Affect User Perceptions and Decisions](https://mlsanity.com/paper/2602.17185/) (2026)
- [How Persuasive Could LLMs Be? A First Study Combining Linguistic-Rhetorical Analysis and User Experiments](https://mlsanity.com/paper/2508.09614/) (2025)
- [Large Language Models Persuade Without Planning Theory of Mind](https://mlsanity.com/paper/2602.17045/) (2026)
- [Perceived Political Bias in LLMs Reduces Persuasive Abilities](https://mlsanity.com/paper/2602.18092/) (2026)
- [Evaluating Large Language Models in a Complex Hidden Role Game](https://mlsanity.com/paper/2605.22826/) (2026)
- [Structuring Collective Action with LLM-Guided Evolution: From Ill-Structured Problems to Executable Heuristics](https://mlsanity.com/paper/2509.20412/) (2025)
- [Spontaneous Persuasion: An Audit of Model Persuasiveness in Everyday Conversations](https://mlsanity.com/paper/2604.22109/) (2026)
- [Assessing and Explaining the Persuadability of Large Language Models as Legal Decision Tools](https://mlsanity.com/paper/2604.26233/) (2026)
- [TourMart: A Parametric Audit Instrument for Commission Steering in LLM Travel Agents](https://mlsanity.com/paper/2605.10440/) (2026)
- [Human Decision-Making with Persuasive and Narrative LLM Explanations](https://mlsanity.com/paper/2605.23867/) (2026)
- [A Model of Multi-turn Human Persuadability Using Probabilistic Belief Tracing](https://mlsanity.com/paper/2606.05330/) (2026)
- [The Closing Window: How Governments Could Lose Their Ability to Restrain Advanced AI](https://mlsanity.com/paper/2608.05173/) (2026)
- [Position: The Alignment Community is Unintentionally Building a Censor's Toolkit](https://mlsanity.com/paper/2608.12346/) (2026)

## Abstract (arXiv)

The rapid rise of Large Language Models (LLMs) has created new disruptive possibilities for persuasive communication, enabling fully-automated, personalized, and interactive content generation at an unprecedented scale. In this paper, we survey the emerging field of LLM-based persuasion, reviewing empirical studies that measure the influence of LLM Systems on human attitudes and behaviors. We categorize applications across domains such as politics, marketing, public health, e-commerce, and charitable giving, finding that such systems have frequently achieved human-level or even superhuman persuasiveness. Synthesizing recent evidence, we identify key factors influencing this effectiveness, including the interaction approach, model scale and capability, prompt design, personalization, and AI source disclosure. Furthermore, we critically examine the experimental designs and success metrics used to evaluate these Systems, distinguishing between direct behavioral outcomes and proxy indicators. Our survey suggests that the current capabilities of LLM-based persuasion pose profound ethical and societal risks, including to information integrity, fairness and inclusion, privacy, and individual autonomy. These risks underscore the urgent need for ethical guidelines and updated regulatory frameworks to avoid the widespread deployment of irresponsible and harmful LLM Systems.