DuplexGen: Adaptive Synthesis of Human-AI Turn-Taking Dialogues
DuplexGen is a framework that calibrates LLM predictions with a small set of human preference annotations to generate scenario-adaptive turn-taking dialogues. In six cooperative and competitive tasks, it aligns much closer with human preferences than uncalibrated prompting or generic human-human training data, showing that human calibration is key.
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[Submitted on 28 Jul 2026]
Title:DuplexGen: Adaptive Synthesis of Human-AI Turn-Taking Dialogues
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Abstract:Turn-taking is a central component of full-duplex interaction. Which turn-taking behaviors are appropriate varies with the scenario, yet current models apply a single norm regardless of context. This limitation originates in their training data: human-human speech corpora capture natural timing phenomena but provide little role grounding or scenario-specific norms, while heuristic or prompted synthesis methods inject turn-taking behaviors without basing them on human preferences. We introduce DuplexGen, a framework for generating dialogues with scenario-adaptive turn-taking by calibrating LLM predictions against a small set of slot-level human preference annotations. In six cooperative and competitive tasks, human turn-taking preferences differ systematically, and DuplexGen aligns substantially more closely with those preferences than uncalibrated prompting or training solely on generic human-human data; a full-duplex model trained on DuplexGen-generated data exhibits distinctive, human-preferred turn-taking behaviors. These results show that human calibration, not corpus scale or prompt design alone, is what allows turn-taking synthesis to be scenario-specific.
Comments: Manuscript under review
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2607.26178 [cs.CL]
(or arXiv:2607.26178v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.26178
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Takyoung Kim [view email] [v1] Tue, 28 Jul 2026 18:36:46 UTC (1,658 KB)
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