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 extracts individual preferences and suggests options that satisfy a significant portion of the members. We apply this system to corporate meeting scheduling. We create synthetic employee profiles and simulate conversations at scale, leveraging LLMs to evaluate the system. Our results indicate efficient coordination with reduced interactions between members and the LLM-based system. The system also effectively refines proposed options over time, ensuring their quality and equity. Finally, we conduct a survey study involving human participants to assess our system's ability to aggregate preferences and reasoning. Our findings show that the system exhibits strong performance in both dimensions.

翻译：在各种工作场景中，如会议安排、协作和项目规划，集体决策至关重要，但由于个体偏好多样、工作重点不同以及成员之间的权力动态，往往面临挑战。为此，我们提出一个利用大规模语言模型的系统，通过管理对话和平衡个体偏好来促进群体决策。该系统能够提取个体偏好，并推荐能令大部分成员满意的选项。我们将该系统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，利用大规模语言模型评估系统性能。结果表明，该系统能实现高效协调，减少成员与基于大规模语言模型的系统之间的交互次数。同时，系统能随时间有效优化建议选项，确保其质量和公平性。最后，我们开展了包含人类参与者的调查研究，评估系统在偏好聚合与推理方面的能力。研究结果显示，该系统在两个维度均表现出色。