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arXiv · 2510.05124

MADS: Multi-Agent Dialogue Simulation for Diverse Persuasion Data Generation

Mingjin Li·Yu Liu·Huayi Liu·Xiang Ye·Chao Jiang·Hongguang Zhang·Yu Ruan
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Abstract

We propose MADS (Multi-Agent Dialogue Simulation), a scalable framework for generating persuasive multi-turn dialogues via agent self-play. MADS employs three coordinated agents: User Agents designed to simulate diverse persona-driven behaviors by leveraging personality signifiers such as Zodiac Signs and MBTI types, a Dialog Agent executing task-oriented persuasion strategies and an Optimization Agent evaluating and refining dialogue outcomes. We further validate its effectiveness through users' Chain-of-Attitude (CoA) modeling and dedicated LLMs' persuasion assessment. This approach enables low-cost generation of training data without human annotation, addressing key industry challenges such as lack of user data, cold-start evaluation difficulties, and prompt inefficiency. Applied to a real-world marketing scenario, MADS significantly improved the persuasion capacity of small LLMs, increasing the organic traffic conversion rate by 22.4% (from 1.83% to 2.24%) , demonstrating clear business value.

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Mingjin Li, Yu Liu, Huayi Liu, Xiang Ye, Chao Jiang, Hongguang Zhang, Yu Ruan. 2025-09-30. MADS: Multi-Agent Dialogue Simulation for Diverse Persuasion Data Generation. https://arxiv.org/abs/2510.05124

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