{"ID":2862569,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.05124","arxiv_id":"2510.05124","title":"MADS: Multi-Agent Dialogue Simulation for Diverse Persuasion Data Generation","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.","short_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...","url_abs":"https://arxiv.org/abs/2510.05124","url_pdf":"https://arxiv.org/pdf/2510.05124v2","authors":"[\"Mingjin Li\",\"Yu Liu\",\"Huayi Liu\",\"Xiang Ye\",\"Chao Jiang\",\"Hongguang Zhang\",\"Yu Ruan\"]","published":"2025-09-30T06:55:39Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.AI\",\"cs.CY\",\"cs.HC\",\"cs.MA\"]","methods":"[\"Large Language Model\",\"Generative Adversarial Network\"]","has_code":false}