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

arXiv: https://arxiv.org/abs/2502.08896 · HTML: https://mlsanity.com/paper/2502.08896/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 46 references that are in the map.
- First posted on arXiv 13 Feb 2025.

## Builds on (most cited first, 23 of 23)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [GLTR: Statistical Detection and Visualization of Generated Text](https://mlsanity.com/paper/1906.04043/) (2019)
- [Automatic Detection of Generated Text is Easiest when Humans are Fooled](https://mlsanity.com/paper/1911.00650/) (2019)
- [Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good](https://mlsanity.com/paper/1906.06725/) (2019)
- [Learning to Break the Loop: Analyzing and Mitigating Repetitions for Neural Text Generation](https://mlsanity.com/paper/2206.02369/) (2022)
- [Proactive Human-Machine Conversation with Explicit Conversation Goals](https://mlsanity.com/paper/1906.05572/) (2019)
- [NormBank: A Knowledge Bank of Situational Social Norms](https://mlsanity.com/paper/2305.17008/) (2023)
- [Towards Exploiting Background Knowledge for Building Conversation Systems](https://mlsanity.com/paper/1809.08205/) (2018)
- [Automatic Argument Quality Assessment -- New Datasets and Methods](https://mlsanity.com/paper/1909.01007/) (2019)
- [Argument Strength is in the Eye of the Beholder: Audience Effects in Persuasion](https://mlsanity.com/paper/1708.09085/) (2017)
- [Achieving Reliable Human Assessment of Open-Domain Dialogue Systems](https://mlsanity.com/paper/2203.05899/) (2022)
- [Retrieval-Enhanced Adversarial Training for Neural Response Generation](https://mlsanity.com/paper/1809.04276/) (2018)
- [AI vs. Human -- Differentiation Analysis of Scientific Content Generation](https://mlsanity.com/paper/2301.10416/) (2023)
- [MentalManip: A Dataset For Fine-grained Analysis of Mental Manipulation in Conversations](https://mlsanity.com/paper/2405.16584/) (2024)
- [Weakly-Supervised Hierarchical Models for Predicting Persuasive Strategies in Good-faith Textual Requests](https://mlsanity.com/paper/2101.06351/) (2021)
- [Artificial Intelligence for Health Message Generation: Theory, Method, and an Empirical Study Using Prompt Engineering](https://mlsanity.com/paper/2212.07507/) (2022)
- [M2P2: Multimodal Persuasion Prediction using Adaptive Fusion](https://mlsanity.com/paper/2006.11405/) (2020)
- [A Syntactically Constrained Bidirectional-Asynchronous Approach for Emotional Conversation Generation](https://mlsanity.com/paper/1806.07000/) (2018)
- [Werewolf Among Us: A Multimodal Dataset for Modeling Persuasion Behaviors in Social Deduction Games](https://mlsanity.com/paper/2212.08279/) (2022)
- [An Unsupervised Domain-Independent Framework for Automated Detection of Persuasion Tactics in Text](https://mlsanity.com/paper/1912.06745/) (2019)
- [Enhanced Detection of Conversational Mental Manipulation Through Advanced Prompting Techniques](https://mlsanity.com/paper/2408.07676/) (2024)
- [Towards Standard Criteria for human evaluation of Chatbots: A Survey](https://mlsanity.com/paper/2105.11197/) (2021)

## Built on by (most cited first, 5 of 5)

- [How Do LLMs Persuade? Linear Probes Can Uncover Persuasion Dynamics in Multi-Turn Conversations](https://mlsanity.com/paper/2508.05625/) (2025)
- [Unraveling SITT: Social Influence Technique Taxonomy and Detection with LLMs](https://mlsanity.com/paper/2506.00061/) (2025)
- [CLAIM: An Intent-Driven Multi-Agent Framework for Analyzing Manipulation in Courtroom Dialogues](https://mlsanity.com/paper/2506.04131/) (2025)
- [A Generalizable Rhetorical Strategy Annotation Model Using LLM-based Debate Simulation and Labelling](https://mlsanity.com/paper/2510.15081/) (2025)
- [Distilling Long-CoT Reasoning through Collaborative Step-wise Multi-Teacher Decoding](https://mlsanity.com/paper/2605.02290/) (2026)

## 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.