Significant advancements have occurred in the application of Large Language Models (LLMs) for various tasks and social simulations. Despite this, their capacities to coordinate within task-oriented social contexts are under-explored. Such capabilities are crucial if LLMs are to effectively mimic human-like social behavior and produce meaningful results. To bridge this gap, we introduce collaborative generative agents, endowing LLM-based Agents with consistent behavior patterns and task-solving abilities. We situate these agents in a simulated job fair environment as a case study to scrutinize their coordination skills. We propose a novel framework that equips collaborative generative agents with human-like reasoning abilities and specialized skills. Our evaluation demonstrates that these agents show promising performance. However, we also uncover limitations that hinder their effectiveness in more complex coordination tasks. Our work provides valuable insights into the role and evolution of LLMs in task-oriented social simulations.

翻译：摘要：大语言模型（LLMs）在各类任务与社会模拟中的应用已取得显著进展。然而，其在任务导向社会情境下的协调能力尚未得到充分探索。若要让LLMs有效模拟类人社会行为并产生有意义的结果，此类能力至关重要。为填补这一空白，我们引入了协作生成智能体，赋予基于LLM的智能体一致的行为模式与任务解决能力。我们将这些智能体置于模拟招聘会环境中作为案例研究，以检验其协调技能。我们提出了一种新颖框架，为协作生成智能体配备类人推理能力与专业技能。评估结果表明，这些智能体展现出有前景的性能。然而，我们也发现了制约其在更复杂协调任务中有效性的局限。本研究为LLMs在任务导向社会模拟中的作用与演进提供了重要见解。