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Significant advancements have occurred in the application of Large LanguageModels (LLMs) for various tasks and social simulations. Despite this, theircapacities to coordinate within task-oriented social contexts areunder-explored. Such capabilities are crucial if LLMs are to effectively mimichuman-like social behavior and produce meaningful results. To bridge this gap,we introduce collaborative generative agents, endowing LLM-based Agents withconsistent behavior patterns and task-solving abilities. We situate theseagents in a simulated job fair environment as a case study to scrutinize theircoordination skills. We propose a novel framework that equips collaborativegenerative 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 morecomplex coordination tasks. Our work provides valuable insights into the roleand evolution of LLMs in task-oriented social simulations.

MetaAgents: Simulating Interactions of Human Behaviors for LLM-based Task-oriented Coordination via Collaborative Generative Agents