@inproceedings{jiang-etal-2026-trace,
title = "{TRACE}: A Corpus of Team Creative Discussions",
author = "Jiang, Yixuan and
Hu, Tiancheng and
Hernandez-Orallo, Jose and
Stillwell, David and
Sun, Luning",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.2111/",
pages = "45535--45552",
ISBN = "979-8-89176-390-6",
abstract = "Understanding how discussion dynamics shape team creativity has been limited by the difficulty of measuring process at scale. We introduce Trace, a corpus of 309 group discussions from 103 teams (460 participants) across six creative problem-solving tasks. The dataset follows an input-process-output framework, integrating team composition (demographics, personalities), full discussion transcripts, and creativity outcomes. Using sentence embeddings and factor analysis, we identify four interpretable discussion dimensions: \textbf{Coherence}, \textbf{Exploration}, \textbf{Convergence}, and \textbf{Participation}. Analysis reveals a depth-breadth trade-off: coherent idea development inversely relates to semantic exploration. Larger teams explore more broadly but converge less effectively while team diversity shapes participation patterns more than discussion content. Novelty and usefulness in the creativity outcomes follow distinct pathways: Exploration and Convergence predict novelty, whereas Coherence predicts usefulness. These findings ground our understanding of how teams talk their way to creative solutions and provide guidance for designing multiagent systems."
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%0 Conference Proceedings
%T TRACE: A Corpus of Team Creative Discussions
%A Jiang, Yixuan
%A Hu, Tiancheng
%A Hernandez-Orallo, Jose
%A Stillwell, David
%A Sun, Luning
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F jiang-etal-2026-trace
%X Understanding how discussion dynamics shape team creativity has been limited by the difficulty of measuring process at scale. We introduce Trace, a corpus of 309 group discussions from 103 teams (460 participants) across six creative problem-solving tasks. The dataset follows an input-process-output framework, integrating team composition (demographics, personalities), full discussion transcripts, and creativity outcomes. Using sentence embeddings and factor analysis, we identify four interpretable discussion dimensions: Coherence, Exploration, Convergence, and Participation. Analysis reveals a depth-breadth trade-off: coherent idea development inversely relates to semantic exploration. Larger teams explore more broadly but converge less effectively while team diversity shapes participation patterns more than discussion content. Novelty and usefulness in the creativity outcomes follow distinct pathways: Exploration and Convergence predict novelty, whereas Coherence predicts usefulness. These findings ground our understanding of how teams talk their way to creative solutions and provide guidance for designing multiagent systems.
%U https://aclanthology.org/2026.acl-long.2111/
%P 45535-45552
Markdown (Informal)
[TRACE: A Corpus of Team Creative Discussions](https://aclanthology.org/2026.acl-long.2111/) (Jiang et al., ACL 2026)
ACL
- Yixuan Jiang, Tiancheng Hu, Jose Hernandez-Orallo, David Stillwell, and Luning Sun. 2026. TRACE: A Corpus of Team Creative Discussions. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 45535–45552, San Diego, California, United States. Association for Computational Linguistics.