自进化对话代理提升服务型对话任务完成率17.6%
SEAD: Self-Evolving Agent for Multi-Turn Service Dialogue
- 分离用户建模为状态控制器与角色扮演模型，动态生成训练场景
- 在真实服务对话中实现17.6%任务完成率提升和11.1%效率优化
- 无需大量人工标注，适合需低成本训练的智能客服研发
大语言模型在开放域对话中表现卓越，但在服务型对话中性能受限，主要因依赖噪声大、质量低的人类对话数据。该问题源于数据稀缺及难以模拟真实的、目标导向的用户行为。为此，我们提出SEAD（Self-Evolving Agent for Service Dialogue），一种无需大规模人工标注即可学习有效策略的框架。SEAD将用户建模解耦为两部分：Profile Controller负责生成多样用户状态以管理训练课程，User Role-play Model专注真实角色扮演。此设计使环境提供自适应训练场景而非不公平对抗。实验表明，SEAD显著优于开源基础模型与闭源商业模型，在任务完成率上提升17.6%，对话效率提升11.1%。代码已公开于：https://github.com/Da1yuqin/SEAD。
原文摘要 · Abstract (English)
Large Language Models have demonstrated remarkable capabilities in open-domain dialogues. However, current methods exhibit suboptimal performance in service dialogues, as they rely on noisy, low-quality human conversation data. This limitation arises from data scarcity and the difficulty of simulating authentic, goal-oriented user behaviors. To address these issues, we propose SEAD (Self-Evolving Agent for Service Dialogue), a framework that enables agents to learn effective strategies without large-scale human annotations. SEAD decouples user modeling into two components: a Profile Controller that generates diverse user states to manage training curriculum, and a User Role-play Model that focuses on realistic role-playing. This design ensures the environment provides adaptive training scenarios rather than acting as an unfair adversary. Experiments demonstrate that SEAD significantly outperforms Open-source Foundation Models and Closed-source Commercial Models, improving task completion rate by 17.6% and dialogue efficiency by 11.1%. Code is available at: https://github.com/Da1yuqin/SEAD.
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