TRACE: A Corpus of Team Creative Discussions
Yixuan Jiang, Tiancheng Hu, José Hernández-Orallo, David Stillwell, Luning Sun
摘要
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 (421 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. 1 * Work done while visiting the University of Cambridge.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
它引用的顶会 Paper3
- MeetingBank: A Benchmark Dataset for Meeting SummarizationYebowen Hu, Timothy Ganter, Hanieh Deilamsalehy, Franck Dernoncourt 等ACL 2023 · 被引用 19 次
- Creativity in LLM-based Multi-Agent Systems: A SurveyYi-Cheng Lin, Kang-Chieh Chen, Zhe-Yan Li, Tzu-Heng Wu 等EMNLP 2025 · 被引用 3 次
- Fora: A corpus and framework for the study of facilitated dialogueHope Schroeder, Deb Roy, Jad KabbaraACL 2024
相关 Paper
- Towards AI as Colleagues: Multi-Agent System Improves Structured Ideation ProcessesKexin Quan, Dina Albassam, Mengke Wu, Zijian Ding 等CHI 2026 · 被引用 2 次
- A Paradigm for Creative OwnershipTejaswi Polimetla, Katy Ilonka Gero, Elena L. GlassmanCHI 2026 · 被引用 3 次
- Representational Similarity and Model Behavior in Multi-Agent InteractionYujin Potter, Seun Eisape, Shiyang Lai, Alexander Huth 等ICML 2026
- Language Grounded Multi-agent Reinforcement Learning with Human-interpretable CommunicationHuao Li, Hossein Nourkhiz Mahjoub, Behdad Chalaki, Vaishnav Tadiparthi 等NeurIPS 2024 · 被引用 31 次
- Beyond Team Makeup: Diversity in Teams Predicts Valued Outcomes in Computer-Mediated CollaborationsAngela E. B. Stewart, Mary Jean Amon, Nicholas D. Duran, Sidney K. D'MelloCHI 2020 · 被引用 15 次
