AI RESEARCH
SEAD: Self-Evolving Agent for Multi-Turn Service Dialogue
arXiv CS.CL
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ArXi:2602.03548v2 Announce Type: replace Large Language Models have nstrated 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.