# The Levers of Political Persuasion with Conversational AI

arXiv: https://arxiv.org/abs/2507.13919 · HTML: https://mlsanity.com/paper/2507.13919/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 59 references that are in the map.
- First posted on arXiv 18 Jul 2025.

## Builds on (most cited first, 19 of 19)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Solving Quantitative Reasoning Problems with Language Models](https://mlsanity.com/paper/2206.14858/) (2022)
- [Enhancing Chat Language Models by Scaling High-quality Instructional Conversations](https://mlsanity.com/paper/2305.14233/) (2023)
- [Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting](https://mlsanity.com/paper/2310.11324/) (2023)
- [Measuring and Improving Consistency in Pretrained Language Models](https://mlsanity.com/paper/2102.01017/) (2021)
- [On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective](https://mlsanity.com/paper/2302.12095/) (2023)
- [Political Compass or Spinning Arrow? Towards More Meaningful Evaluations for Values and Opinions in Large Language Models](https://mlsanity.com/paper/2402.16786/) (2024)
- [Generative Language Models and Automated Influence Operations: Emerging Threats and Potential Mitigations](https://mlsanity.com/paper/2301.04246/) (2023)
- [Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC](https://mlsanity.com/paper/1507.04544/) (2015)
- [On the Conversational Persuasiveness of Large Language Models: A Randomized Controlled Trial](https://mlsanity.com/paper/2403.14380/) (2024)
- [Thousands of AI Authors on the Future of AI](https://mlsanity.com/paper/2401.02843/) (2024)
- [A Mechanism-Based Approach to Mitigating Harms from Persuasive Generative AI](https://mlsanity.com/paper/2404.15058/) (2024)
- [Artificial Influence: An Analysis Of AI-Driven Persuasion](https://mlsanity.com/paper/2303.08721/) (2023)
- [Persuasion with Large Language Models: A Survey of Empirical Evidence, Study Methodologies, and Ethical Implications](https://mlsanity.com/paper/2411.06837/) (2024)
- [Lies, Damned Lies, and Distributional Language Statistics: Persuasion and Deception with Large Language Models](https://mlsanity.com/paper/2412.17128/) (2024)
- [When Large Language Models are More PersuasiveThan Incentivized Humans, and Why](https://mlsanity.com/paper/2505.09662/) (2025)
- [A Framework to Assess the Persuasion Risks Large Language Model Chatbots Pose to Democratic Societies](https://mlsanity.com/paper/2505.00036/) (2025)

## Built on by (most cited first, 20 of 20)

- [Technological folie `a deux: Feedback Loops Between AI Chatbots and Mental Illness](https://mlsanity.com/paper/2507.19218/) (2025)
- [Beyond One-Way Influence: Bidirectional Opinion Dynamics in Multi-Turn Human-LLM Interactions](https://mlsanity.com/paper/2510.20039/) (2025)
- [Emergent Alignment via Competition](https://mlsanity.com/paper/2509.15090/) (2025)
- [AI Propaganda factories with language models](https://mlsanity.com/paper/2508.20186/) (2025)
- [Architecting Trust in Artificial Epistemic Agents](https://mlsanity.com/paper/2603.02960/) (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)
- [KnowMT-Bench: Benchmarking Knowledge-Intensive Long-Form Question Answering in Multi-Turn Dialogues](https://mlsanity.com/paper/2509.21856/) (2025)
- [Tailored untruths: How personalisation challenges LLM safeguards](https://mlsanity.com/paper/2510.12993/) (2025)
- [A Crowdsourced Study of ChatBot Influence in Value-Driven Decision Making Scenarios](https://mlsanity.com/paper/2511.15857/) (2025)
- [Large language models can effectively convince people to believe conspiracies](https://mlsanity.com/paper/2601.05050/) (2026)
- [How Open Must Language Models be to Enable Reliable Scientific Inference?](https://mlsanity.com/paper/2603.26539/) (2026)
- [Talk2AI: A Longitudinal Dataset of Human--AI Persuasive Conversations](https://mlsanity.com/paper/2604.04354/) (2026)
- [Using Cognitive Models to Improve Language Model Simulation of Human Persuasion Games](https://mlsanity.com/paper/2606.17657/) (2026)
- [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)
- [Addressing Climate Action Misperceptions with Generative AI](https://mlsanity.com/paper/2602.22564/) (2026)
- [DeliberationBench: A Normative Benchmark for the Influence of Large Language Models on Users' Views](https://mlsanity.com/paper/2603.10018/) (2026)
- [Spontaneous Persuasion: An Audit of Model Persuasiveness in Everyday Conversations](https://mlsanity.com/paper/2604.22109/) (2026)
- [A Model of Multi-turn Human Persuadability Using Probabilistic Belief Tracing](https://mlsanity.com/paper/2606.05330/) (2026)
- [A Multi-Stage Agentic Framework for Effective Counter-Narrative Generation and Refinement](https://mlsanity.com/paper/2609.14178/) (2026)

## Abstract (arXiv)

There are widespread fears that conversational AI could soon exert unprecedented influence over human beliefs. Here, in three large-scale experiments (N=76,977), we deployed 19 LLMs-including some post-trained explicitly for persuasion-to evaluate their persuasiveness on 707 political issues. We then checked the factual accuracy of 466,769 resulting LLM claims. Contrary to popular concerns, we show that the persuasive power of current and near-future AI is likely to stem more from post-training and prompting methods-which boosted persuasiveness by as much as 51% and 27% respectively-than from personalization or increasing model scale. We further show that these methods increased persuasion by exploiting LLMs' unique ability to rapidly access and strategically deploy information and that, strikingly, where they increased AI persuasiveness they also systematically decreased factual accuracy.