| title | DuplexGen: Adaptive Synthesis of Human-AI Turn-Taking Dialogues |
|---|---|
| authors | Takyoung Kim, Kang-wook Kim, Sang Hoon Woo, Julia Hirschberg, Gunhee Kim, Dilek Hakkani-Tür |
| date | 2026-07-28 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2607.26178 |
| affiliation | University of Illinois at Urbana-Champaign |
| upvotes | 11 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| 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. |
cite
@misc{kim2026duplexgen,
title={DuplexGen: Adaptive Synthesis of Human-AI Turn-Taking Dialogues},
author={Takyoung Kim and Kang-wook Kim and Sang Hoon Woo and Julia Hirschberg and Gunhee Kim and Dilek Hakkani-Tür},
year={2026},
eprint={2607.26178},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2607.26178}
}