MADS: Multi-Agent Dialogue Simulation for Diverse Persuasion Data Generation
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909836355043328 |
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| author | Li, Mingjin Liu, Yu Liu, Huayi Ye, Xiang Jiang, Chao Zhang, Hongguang Ruan, Yu |
| author_facet | Li, Mingjin Liu, Yu Liu, Huayi Ye, Xiang Jiang, Chao Zhang, Hongguang Ruan, Yu |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_05124 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MADS: Multi-Agent Dialogue Simulation for Diverse Persuasion Data Generation Li, Mingjin Liu, Yu Liu, Huayi Ye, Xiang Jiang, Chao Zhang, Hongguang Ruan, Yu Computation and Language Artificial Intelligence Computers and Society Human-Computer Interaction Multiagent Systems 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. |
| title | MADS: Multi-Agent Dialogue Simulation for Diverse Persuasion Data Generation |
| topic | Computation and Language Artificial Intelligence Computers and Society Human-Computer Interaction Multiagent Systems |
| url | https://arxiv.org/abs/2510.05124 |