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.

应用大型语言模型（LLMs）在各种任务和社会模拟中取得了显著进展，但它们在任务导向的社会背景中的协调能力尚未得到充分探索。为了弥合这一差距，我们引入了协作生成代理，为基于 LLMs 的代理赋予一致的行为模式和解决任务的能力。我们将这些代理置于一个模拟的招聘会环境中进行案例研究，以审查它们的协调能力。我们提出了一个新颖的框架，赋予协作生成代理人类般的推理能力和专业技能。我们的评估结果表明，这些代理显示出有希望的性能。然而，我们也发现了限制，阻碍了它们在更复杂的协调任务中的效果。我们的工作对于 LLMs 在任务导向的社会模拟中的作用和发展提供了有价值的见解。