TIDES：用于建模多方社会动态的纵向双语数据集
TIDES: A Longitudinal Bilingual Dataset for Modeling Multi-Party Social Dynamics
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中文总结 AI 辅助
研究针对现有群体对话数据集无法捕捉团队长期社会动态的问题，推出TIDES纵向双语数据集，基于其微调模型提升下一个说话人预测性能，同时发现预测效果与话语自然度存在潜在不匹配。
中文摘要 AI 辅助
群体对话是人类协作的基础，但标准大语言模型（LLMs）仍难以应对多方互动的复杂性。这一挑战持续存在，部分原因是现有的群体对话数据集往往局限于短期实验室环境中的人为任务，无法捕捉现实世界团队的长期社会动态。为弥合这一差距，我们推出TIDES——一个高分辨率纵向数据集，跟踪12个大学项目团队一整个学期的情况。TIDES包含来自面对面会议的英语和韩语共75971条话语，提供了团队开展自主管理项目的自然记录。我们的社会结构标注涵盖互动类型、涌现角色和发展阶段，支持对团队数月来的演变进行建模。实验表明，在TIDES上进行微调，使下一个说话人预测相比二元语法基线（64.53%）提升了13.8个百分点，且性能可与强大的专有零样本模型相媲美；该模型在AMI会议语料库上的表现与已发表的最先进水平仅差2.1个百分点，同时使用的训练数据减少了约42%。然而，人工评估显示，更好的下一个说话人预测并不一定产生更自然或连贯的话语，因为微调模型通常不如原始模型受欢迎。这种潜在的不匹配促使进一步研究结构建模如何支持自然的多方生成。
英文摘要
Group conversations are fundamental to human collaboration, yet standard large language models (LLMs) still struggle with the complexities of multi-party interaction. This challenge persists in part because existing group conversation datasets are often limited to short-term lab settings with contrived tasks, failing to capture the long-term social dynamics of real-world teams. To bridge this gap, we introduce TIDES, a high-resolution longitudinal dataset tracking 12 university project teams over a full semester. Comprising 75,971 utterances in both English and Korean from in-person meetings, TIDES provides a naturalistic record of teams working on self-managed projects. Our socio-structural annotations-covering interaction types, emergent roles, and development stages-allow for modeling of team evolution over months. Experiments show that fine-tuning on TIDES improves next-speaker prediction by 13.8 percentage points over a bigram baseline (64.53%) and yields performance comparable to strong proprietary zero-shot models. The model also comes within 2.1 percentage points of the published state of the art on the AMI Meeting Corpus while using approximately 42% less training data. However, human evaluations suggest that better next-speaker prediction does not necessarily yield more natural or coherent utterances, as fine-tuned models were generally less preferred than vanilla models. This potential mismatch motivates further study of how structural modeling can support natural multi-party generation.
发表机构
- KAIST（韩国科学技术院）
- SkillBench
机构由 AI 辅助整理，请以论文原文为准。
