MADS：用于多样化说服数据生成的多智能体对话模拟
MADS: Multi-Agent Dialogue Simulation for Diverse Persuasion Data Generation
AI总结：
提出MADS多智能体对话模拟框架，通过用户、对话和优化三智能体协作生成多样化说服数据，无需人工标注，并在真实营销场景中将小模型自然流量转化率提升22.4%。
摘要：
我们提出了MADS（多智能体对话模拟），这是一个通过智能体自博弈生成有说服力的多轮对话的可扩展框架。MADS采用三个协调的智能体：用户智能体，旨在通过利用星座和MBTI类型等人格标志来模拟多样化的、由人格驱动的行为；对话智能体，执行面向任务的说服策略；以及优化智能体，评估并改进对话结果。我们进一步通过用户的链式态度（CoA）建模和专门的LLM说服评估来验证其有效性。该方法能够以低成本生成训练数据，无需人工标注，解决了缺乏用户数据、冷启动评估困难和提示效率低下等关键行业挑战。应用于真实营销场景中，MADS显著提升了小型LLM的说服能力，将自然流量转化率提高了22.4%（从1.83%提升至2.24%），展示了明确的商业价值。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
