SEAD：多轮服务对话的自演化代理
SEAD: Self-Evolving Agent for Multi-Turn Service Dialogue
AI总结：
SEAD通过自演化机制提升服务对话性能，无需大规模人工标注，实验显示其任务完成率和对话效率均优于开源和商用模型。
摘要：
大型语言模型在开放域对话中表现出色，但在服务对话中表现欠佳，因依赖低质量人类对话数据。为解决此问题，我们提出SEAD框架，使代理无需大规模人工标注即可学习有效策略。SEAD将用户建模分为两个组件：Profile Controller生成多样化用户状态以管理训练课程，User Role-play Model专注于真实角色扮演。此设计确保环境提供适应性训练场景而非不公平对手。实验表明，SEAD在任务完成率和对话效率上分别提升了17.6%和11.1%。代码可访问：https://github.com/Da1yuqin/SEAD。
英文摘要：
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.
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。
