mlsanityData to 7 Oct 2026

Communication is All You Need: Persuasion Dataset Construction via Multi-LLM Communication

arXiv 2502.08896 ↗First posted 13 Feb 2025cs.CL cs.AIcited by 5

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Abstract (arXiv)

Large Language Models (LLMs) have shown proficiency in generating persuasive dialogue, yet concerns about the fluency and sophistication of their outputs persist. This paper presents a multi-LLM communication framework designed to enhance the generation of persuasive data automatically. This framework facilitates the efficient production of high-quality, diverse linguistic content with minimal human oversight. Through extensive evaluations, we demonstrate that the generated data excels in naturalness, linguistic diversity, and the strategic use of persuasion, even in complex scenarios involving social taboos. The framework also proves adept at generalizing across novel contexts. Our results highlight the framework's potential to significantly advance research in both computational and social science domains concerning persuasive communication.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 23 of its 46 references that are in the map. First posted on arXiv 13 Feb 2025.

Builds on

23 of 46 references.

References · 23

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11 more
  1. 2018Retrieval-Enhanced Adversarial Training for Neural Response Generationcited by 22
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  3. 2024MentalManip: A Dataset For Fine-grained Analysis of Mental Manipulation in Conversationscited by 21
  4. 2021Weakly-Supervised Hierarchical Models for Predicting Persuasive Strategies in Good-faith Textual Requestscited by 16
  5. 2022Artificial Intelligence for Health Message Generation: Theory, Method, and an Empirical Study Using Prompt Engineeringcited by 14
  6. 2020M2P2: Multimodal Persuasion Prediction using Adaptive Fusioncited by 12
  7. 2018A Syntactically Constrained Bidirectional-Asynchronous Approach for Emotional Conversation Generationcited by 11
  8. 2022Werewolf Among Us: A Multimodal Dataset for Modeling Persuasion Behaviors in Social Deduction Gamescited by 11
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Cited by year (2026 to 7 Oct)

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

5 papers.