# Large Language Models are as persuasive as humans, but how? About the cognitive effort and moral-emotional language of LLM arguments

arXiv: https://arxiv.org/abs/2404.09329 · HTML: https://mlsanity.com/paper/2404.09329/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 3 references that are in the map.
- First posted on arXiv 14 Apr 2024.

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

- [Human Decision-making is Susceptible to AI-driven Manipulation](https://mlsanity.com/paper/2502.07663/) (2025)
- [Synthetic Socratic Debates: Examining Persona Effects on Moral Decision and Persuasion Dynamics](https://mlsanity.com/paper/2506.12657/) (2025)
- [Lies, Damned Lies, and Distributional Language Statistics: Persuasion and Deception with Large Language Models](https://mlsanity.com/paper/2412.17128/) (2024)
- [Must Read: A Comprehensive Survey of Computational Persuasion](https://mlsanity.com/paper/2505.07775/) (2025)
- [A Simulation System Towards Solving Societal-Scale Manipulation](https://mlsanity.com/paper/2410.13915/) (2024)
- [ToMAP: Training Opponent-Aware LLM Persuaders with Theory of Mind](https://mlsanity.com/paper/2505.22961/) (2025)
- [Toward Holistic Evaluation of Recommender Systems Powered by Generative Models](https://mlsanity.com/paper/2504.06667/) (2025)
- [Large Language Models for Subjective Language Understanding: A Survey](https://mlsanity.com/paper/2508.07959/) (2025)
- [Beyond Persuasion: Towards Conversational Recommender System with Credible Explanations](https://mlsanity.com/paper/2409.14399/) (2024)
- [Exploring the Impact of Personality Traits on Conversational Recommender Systems: A Simulation with Large Language Models](https://mlsanity.com/paper/2504.12313/) (2025)
- [Persuasion Games using Large Language Models](https://mlsanity.com/paper/2408.15879/) (2024)
- [How Do LLMs Persuade? Linear Probes Can Uncover Persuasion Dynamics in Multi-Turn Conversations](https://mlsanity.com/paper/2508.05625/) (2025)
- [Affective Multimodal Agents with Proactive Knowledge Grounding for Emotionally Aligned Marketing Dialogue](https://mlsanity.com/paper/2511.21728/) (2025)
- [A Survey on Moral Foundation Theory and Pre-Trained Language Models: Current Advances and Challenges](https://mlsanity.com/paper/2409.13521/) (2024)
- [Dynamic Knowledge Integration for Evidence-Driven Counter-Argument Generation with Large Language Models](https://mlsanity.com/paper/2503.05328/) (2025)
- [Toward Reasonable Parrots: Why Large Language Models Should Argue with Us by Design](https://mlsanity.com/paper/2505.05298/) (2025)
- [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)
- [MADS: Multi-Agent Dialogue Simulation for Diverse Persuasion Data Generation](https://mlsanity.com/paper/2510.05124/) (2025)
- [Talk2AI: A Longitudinal Dataset of Human--AI Persuasive Conversations](https://mlsanity.com/paper/2604.04354/) (2026)
- [Misinformation Propagation in Benign Multi-Agent Systems](https://mlsanity.com/paper/2606.16710/) (2026)
- [Working with Large Language Models to Enhance Messaging Effectiveness for Vaccine Confidence](https://mlsanity.com/paper/2504.09857/) (2025)
- [How Persuasive Could LLMs Be? A First Study Combining Linguistic-Rhetorical Analysis and User Experiments](https://mlsanity.com/paper/2508.09614/) (2025)
- [When Agents Persuade: Rhetoric Generation and Mitigation in LLMs](https://mlsanity.com/paper/2603.04636/) (2026)
- [Does AI and Human Advice Mitigate Punishment for Selfish Behavior? An Experiment on AI ethics From a Psychological Perspective](https://mlsanity.com/paper/2507.19487/) (2025)
- [Building Resilient Information Ecosystems: Large LLM-Generated Dataset of Persuasion Attacks](https://mlsanity.com/paper/2511.19488/) (2025)
- [Spontaneous Persuasion: An Audit of Model Persuasiveness in Everyday Conversations](https://mlsanity.com/paper/2604.22109/) (2026)
- [LLM Wardens: Mitigating Adversarial Persuasion with Third-Party Conversational Oversight](https://mlsanity.com/paper/2605.08321/) (2026)
- [How Far Did They Go? The Persuasive Tactics of Covert LLM Agents in a Discontinued Field Experiment](https://mlsanity.com/paper/2606.05256/) (2026)
- [Belief Cascades Drive Persuasion in LLM Agent Networks](https://mlsanity.com/paper/2608.25152/) (2026)
- [Evaluating the Capabilities of LLMs for Persuasive Dialogue](https://mlsanity.com/paper/2608.29738/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) are already as persuasive as humans. However, we know very little about how they do it. This paper investigates the persuasion strategies of LLMs, comparing them with human-generated arguments. Using a dataset of 1,251 participants in an experiment, we analyze the persuasion strategies of LLM-generated and human-generated arguments using measures of cognitive effort (lexical and grammatical complexity) and moral-emotional language (sentiment and moral analysis). The study reveals that LLMs produce arguments that require higher cognitive effort, exhibiting more complex grammatical and lexical structures than human counterparts. Additionally, LLMs demonstrate a significant propensity to engage more deeply with moral language, utilizing both positive and negative moral foundations more frequently than humans. In contrast with previous research, no significant difference was found in the emotional content produced by LLMs and humans. These findings contribute to the discourse on AI and persuasion, highlighting the dual potential of LLMs to both enhance and undermine informational integrity through communication strategies for digital persuasion.