From What to Respond to When to Respond: Timely Response Generation for Open-domain Dialogue Agents
Existing dialogue response generation research focuses on *what* to generate, neglecting the critical temporal decision problem of *when* to respond. This paper formally introduces “timely dialogue response generation” as a novel task for open-domain conversational agents. Methodologically, we construct TimelyChat—the first temporally enhanced evaluation benchmark—and a 55K-event-driven dialogue dataset; propose a time-aware dialogue generation paradigm; and design Timer, an end-to-end model that jointly models response content and response timing. Timer integrates temporal commonsense knowledge graph mining with large language model–based data synthesis to enable response interval prediction and time-aligned generation. Experiments demonstrate that Timer significantly outperforms prompt-engineered LLMs and diverse fine-tuned baselines in both turn-level and dialogue-level evaluations. All data, models, and code are publicly released.