  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.
