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.

翻译：大型语言模型（LLMs）凭借其广泛的世界知识和语言相关任务的娴熟能力，已成为推理、规划与决策的关键工具。因此，LLMs在多智能体系统中具备促进自然语言交互以实现协作的巨大潜力。然而，LLM智能体倾向于过度报告和服从任何指令，这可能导致多智能体协作中的信息冗余与混乱。受人类组织启发，本文提出一个框架，通过向LLM智能体施加基于提示的组织结构来缓解这些问题。通过一系列涉及具身LLM智能体与人机协作的实验，我们的结果揭示了指定领导力对团队效率的影响，阐明了LLM智能体展现的领导素质及其自发协作行为。此外，我们利用LLMs的潜力，通过"批判-反思"过程提出增强型组织提示，从而产生新型组织结构，降低通信成本并提升团队效率。