In various work contexts, such as meeting scheduling, collaborating, and project planning, collective decision-making is essential but often challenging due to diverse individual preferences, varying work focuses, and power dynamics among members. To address this, we propose a system leveraging Large Language Models (LLMs) to facilitate group decision-making by managing conversations and balancing preferences among individuals. Our system aims to extract individual preferences from conversations and suggest options that satisfy the preferences of the members. We specifically apply this system to corporate meeting scheduling. We create synthetic employee profiles and simulate conversations at scale, leveraging LLMs to evaluate the system performance as a novel approach to conducting a user study. Our results indicate efficient coordination with reduced interactions between the members and the LLM-based system. The system refines and improves its proposed options over time, ensuring that many of the members' individual preferences are satisfied in an equitable way. Finally, we conduct a survey study involving human participants to assess our system's ability to aggregate preferences and reasoning about them. Our findings show that the system exhibits strong performance in both dimensions.

翻译：在各种工作场景中，例如会议安排、协作与项目规划，集体决策至关重要，但由于成员间个人偏好差异、工作重点不同以及权力动态的影响，这一过程往往颇具挑战。为此，我们提出一种借助大型语言模型（LLMs）的系统，通过管理对话并平衡个体偏好来促进群体决策。该系统旨在从对话中提取个体偏好，并提出能够满足成员偏好的选项。我们将该系统特别应用于企业会议安排场景。通过创建合成员工档案并大规模模拟对话，我们利用LLMs评估系统性能，这是一种开展用户研究的新方法。结果表明，系统能够高效协调，减少成员与LLM系统之间的交互次数。系统会随时间不断优化和调整其提议的选项，确保以公平方式满足多数成员的个体偏好。最后，我们开展了一项包含人类参与者的调查研究，评估系统在偏好聚合及其推理能力方面的表现。研究发现，该系统在这两个维度均展现出优异性能。