SEAD: Self-Evolving Agent for Multi-Turn Service Dialogue
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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Yuqin Dai
claimed_verified
2026-02-09T08:34:30.731Z
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Yuqin Dai
dayll
user
dayll
6986d4b52d626112378ad660
Ning Gao
claimed_verified
2026-02-09T08:34:26.859Z
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高宁
Aoyama7Hai
user
Aoyama7Hai
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Wei Zhang
6986d4b52d626112378ad662
Jie Wang
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Zichen Luo
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Jinpeng Wang
6986d4b52d626112378ad665
Yujie Wang
6986d4b52d626112378ad666
Ruiyuan Wu
6986d4b52d626112378ad667
Chaozheng Wang
SEAD: Self-Evolving Agent for Multi-Turn Service Dialogue
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.
SEAD framework enables service dialogue agents to learn effective strategies through self-evolving user modeling components, achieving superior task completion and dialogue efficiency compared to existing foundation and commercial models.
large language models service dialogues user modeling profile controller user role-play model task completion rate dialogue efficiency