@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://preview.aclanthology.org/ingest-acl/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."
}