SEAD: Self-Evolving Agent for Multi-Turn Service Dialogue
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arXiv
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| Format: | Preprint |
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2026
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| author | Dai, Yuqin Gao, Ning Zhang, Wei Wang, Jie Luo, Zichen Wang, Jinpeng Wang, Yujie Wu, Ruiyuan Wang, Chaozheng |
| author_facet | Dai, Yuqin Gao, Ning Zhang, Wei Wang, Jie Luo, Zichen Wang, Jinpeng Wang, Yujie Wu, Ruiyuan Wang, Chaozheng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_03548 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SEAD: Self-Evolving Agent for Multi-Turn Service Dialogue Dai, Yuqin Gao, Ning Zhang, Wei Wang, Jie Luo, Zichen Wang, Jinpeng Wang, Yujie Wu, Ruiyuan Wang, Chaozheng Computation and Language 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. |
| title | SEAD: Self-Evolving Agent for Multi-Turn Service Dialogue |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2602.03548 |