用多智能体自对弈生成多样说服对话，低成本提升小模型转化率
MADS: Multi-Agent Dialogue Simulation for Diverse Persuasion Data Generation
- 三智能体协作：用户、对话、优化器分工模拟不同人格与说服策略
- 真实营销场景中使小模型转化率提升22.4%（1.83%→2.24%）
- 无需人工标注，适合缺数据的推广与冷启动场景
我们提出MADS（多智能体对话模拟）框架，通过智能体自对弈实现可扩展的说服性多轮对话生成。MADS包含三个协同智能体：基于星座、MBTI等性格标识模拟多样化用户行为的用户智能体，执行任务导向说服策略的对话智能体，以及评估并优化对话结果的优化智能体。通过用户态度链（CoA）建模和专用大模型评估验证其有效性。该方法无需人工标注即可低成本生成训练数据，解决用户数据匮乏、冷启动评估难、提示效率低等行业难题。在真实营销场景中，显著提升小模型的说服能力，使有机流量转化率从1.83%提升至2.24%，提高22.4%，展现明确商业价值。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。