AutoPersuade: A Framework for Evaluating and Explaining Persuasive Arguments
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Abstract (arXiv)
We introduce AutoPersuade, a three-part framework for constructing persuasive messages. First, we curate a large dataset of arguments with human evaluations. Next, we develop a novel topic model to identify argument features that influence persuasiveness. Finally, we use this model to predict the effectiveness of new arguments and assess the causal impact of different components to provide explanations. We validate AutoPersuade through an experimental study on arguments for veganism, demonstrating its effectiveness with human studies and out-of-sample predictions.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 14 of its 39 references that are in the map. First posted on arXiv 11 Oct 2024.
Builds on
14 of 39 references.
References · 14
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
- 2016CVXPY: A Python-Embedded Modeling Language for Convex Optimizationcited by 641
- 2019Introduction to Multi-Armed Banditscited by 481
- 2022Text and Code Embeddings by Contrastive Pre-Trainingcited by 361
- 2019Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Goodcited by 209
- 2021Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyondcited by 178
- 2016Winning Arguments: Interaction Dynamics and Persuasion Strategies in Good-faith Online Discussionscited by 110
- 2018How to Make Causal Inferences Using Textscited by 59
- 2020Aspect-Controlled Neural Argument Generationcited by 43
- 2022Efficient computation of rankings from pairwise comparisonscited by 31
- 2019Are You Convinced? Choosing the More Convincing Evidence with a Siamese Networkcited by 29
- 2024Can Language Models Recognize Convincing Arguments?cited by 21
2 more
- 2020Quantifying the Causal Effects of Conversational Tendenciescited by 12
- 2023ArgU: A Controllable Factual Argument Generatorcited by 7
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Citing papers per year: 2024: 1; 2025: 2; 2026: 4.
7 papers.
- 2024Persuasion with Large Language Models: A Survey of Empirical Evidence, Study Methodologies, and Ethical Implicationscited by 32
- 2025Must Read: A Comprehensive Survey of Computational Persuasioncited by 13
- 2025Persuasion Should be Double-Blind: A Multi-Domain Dialogue Dataset With Faithfulness Based on Causal Theory of Mindcited by 8
- 2026Detecting Winning Arguments with Large Language Models and Persuasion Strategiescited by 3
- 2026Can One-sided Arguments Lead to Response Change in Large Language Models?cited by 0
- 2026ARGUS: Seeing the Influence of Narrative Features on Persuasion in Argumentative Textscited by 0
- 2026MA2P: A Meta-Cognitive Autonomous Intelligent Agents Framework for Complex Persuasioncited by 0
- 2026MA2P: A Meta-Cognitive Autonomous Intelligent Agents Framework for Complex Persuasion18 May 2026
- 2026ARGUS: Seeing the Influence of Narrative Features on Persuasion in Argumentative Texts27 Feb 2026
- 2026Can One-sided Arguments Lead to Response Change in Large Language Models?5 Feb 2026
- 2026Detecting Winning Arguments with Large Language Models and Persuasion Strategies15 Jan 2026
- 2025Must Read: A Comprehensive Survey of Computational Persuasion12 May 2025
- 2025Persuasion Should be Double-Blind: A Multi-Domain Dialogue Dataset With Faithfulness Based on Causal Theory of Mind28 Feb 2025
- 2024Persuasion with Large Language Models: A Survey of Empirical Evidence, Study Methodologies, and Ethical Implications11 Nov 2024