Embodied LLM Agents Learn to Cooperate in Organized Teams
Xudong Guo Kaixuan Huang Jiale Liu Wenhui Fan Natalia Vélez Qingyun Wu Huazheng Wang Thomas L. Griffiths Mengdi Wang
Large Language Models (LLMs) have emerged as integral tools for reasoning,
planning, and decision-making, drawing upon their extensive world knowledge and
proficiency in language-related tasks. LLMs thus hold tremendous potential for
natural language interaction within multi-agent systems to foster cooperation.
However, LLM agents tend to over-report and comply with any instruction, which
may result in information redundancy and confusion in multi-agent cooperation.
Inspired by human organizations, this paper introduces a framework that imposes
prompt-based organization structures on LLM agents to mitigate these problems.
Through a series of experiments with embodied LLM agents and human-agent
collaboration, our results highlight the impact of designated leadership on
team efficiency, shedding light on the leadership qualities displayed by LLM
agents and their spontaneous cooperative behaviors. Further, we harness the
potential of LLMs to propose enhanced organizational prompts, via a
Criticize-Reflect process, resulting in novel organization structures that
reduce communication costs and enhance team efficiency.