Controlling Large Language Model-based Agents for Large-Scale Decision-Making: An Actor-Critic Approach

Understand

The remarkable progress in Large Language Models (LLMs) opens up new avenues for addressing planning and decision-making problems in Multi-Agent Systems (MAS).

• However, as the number of agents increases, the issues of hallucination in LLMs and coordination in MAS have become increasingly prominent.
• Additionally, the efficient utilization of tokens emerges as a critical consideration when employing LLMs to facilitate the interactions among a substantial number of agents.
• In this paper, we develop a modular framework called LLaMAC to mitigate these challenges.