Bringing Everyone to the Table: An Experimental Study of LLM-Facilitated Group Decision Making

TL;DR

This study investigates how large language models (LLMs) can facilitate group decision-making by increasing information sharing among members, with an experimental comparison to human facilitation and no facilitation.

Contribution

It provides empirical evidence that LLMs can enhance group discussion engagement without affecting decision outcomes, and introduces an open-source platform for further research.

Findings

LLM facilitation raises minimum engagement levels in discussions.

Increased information sharing does not significantly change final decisions.

LLMs offer a cost-effective alternative to human facilitators.

Abstract

Group decision-making often suffers from uneven information sharing, hindering decision quality. While large language models (LLMs) have been widely studied as aids for individuals, their potential to support groups of users, potentially as facilitators, is relatively underexplored. We present a pre-registered randomized experiment with 1,475 participants assigned to 281 five-person groups completing a hidden profile task--selecting an optimal city for a hypothetical sporting event--under one of four facilitation conditions: no facilitation, a one-time message prompting information sharing, a human facilitator, or an LLM (GPT-4o) facilitator. We find that LLM facilitation increases information shared within a discussion by raising the minimum level of engagement with the task among group members, and that these gains come at limited cost in terms of participants' attitudes towards the task, their group, or their facilitator. Whether by human or AI, there is no significant effect of facilitation on the final decision outcome, suggesting that even substantial but partial increases in information sharing are insufficient to overcome the hidden profile effect studied. To support further research into how LLM-based interfaces can support the future of collaborative decision making, we release our experimental platform, the Group-AI Interaction Laboratory (GRAIL), as an open-source tool.