MetaAgents: Simulating Interactions of Human Behaviors for LLM-based
Task-oriented Coordination via Collaborative Generative Agents
- URL: http://arxiv.org/abs/2310.06500v1
- Date: Tue, 10 Oct 2023 10:17:58 GMT
- Title: MetaAgents: Simulating Interactions of Human Behaviors for LLM-based
Task-oriented Coordination via Collaborative Generative Agents
- Authors: Yuan Li, Yixuan Zhang, and Lichao Sun
- Abstract summary: We introduce collaborative generative agents, endowing LLM-based Agents with consistent behavior patterns and task-solving abilities.
We propose a novel framework that equips collaborative generative agents with human-like reasoning abilities and specialized skills.
Our work provides valuable insights into the role and evolution of Large Language Models in task-oriented social simulations.
- Score: 27.911816995891726
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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