mlsanityData to 14 Oct 2026

AutoPersuade: A Framework for Evaluating and Explaining Persuasive Arguments

arXiv 2410.08917 ↗First posted 11 Oct 2024cs.CLcited by 7

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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

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  2. 2016CVXPY: A Python-Embedded Modeling Language for Convex Optimizationcited by 641
  3. 2019Introduction to Multi-Armed Banditscited by 481
  4. 2022Text and Code Embeddings by Contrastive Pre-Trainingcited by 361
  5. 2019Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Goodcited by 209
  6. 2021Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyondcited by 178
  7. 2016Winning Arguments: Interaction Dynamics and Persuasion Strategies in Good-faith Online Discussionscited by 110
  8. 2018How to Make Causal Inferences Using Textscited by 59
  9. 2020Aspect-Controlled Neural Argument Generationcited by 43
  10. 2022Efficient computation of rankings from pairwise comparisonscited by 31
  11. 2019Are You Convinced? Choosing the More Convincing Evidence with a Siamese Networkcited by 29
  12. 2024Can Language Models Recognize Convincing Arguments?cited by 21
2 more
  1. 2020Quantifying the Causal Effects of Conversational Tendenciescited by 12
  2. 2023ArgU: A Controllable Factual Argument Generatorcited by 7

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 4.

7 papers.