How can LLMs generate diverse team behaviors?
Algorithmic Prompt Generation for Diverse Human-like Teaming and Communication with Large Language Models
This paper explores generating diverse and effective teaming behaviors in multi-agent systems, particularly those powered by Large Language Models (LLMs), without relying on extensive human data collection. It introduces PLAN-QD, a framework that combines LLM-driven agents with Quality Diversity (QD) optimization. QD guides prompt generation and mutation, leading to LLMs exhibiting diverse communication and collaboration strategies. The research validates the approach through a human study and simulated "Steakhouse" environment, showing that PLAN-QD generates broader behavioral diversity than baseline methods and replicates communication-influenced trends observed in human teams. Key to LLM-based multi-agent systems is the algorithmic prompt generation method that automates the process of personality prompt design, allowing diverse agent behaviors to emerge without manual intervention. The research demonstrates the potential of combining LLMs with QD for exploring complex multi-agent interactions.