How can LLMs build efficient multi-agent systems?
MAS-GPT: Training LLMs to Build LLM-based Multi-Agent Systems
This paper introduces MAS-GPT, a large language model (LLM) trained to generate entire multi-agent systems (MAS) tailored to specific user queries, represented as executable Python code. This simplifies MAS creation, improves adaptability to diverse tasks, and reduces the high inference costs associated with existing methods that require manual design or multiple LLM calls. Key points include representing MAS as executable code, a consistency-oriented data construction pipeline focusing on both inter- and intra-consistency of query-MAS pairs, and supervised fine-tuning of a medium-sized LLM to generate these query-specific MAS. The approach shows promising results on various benchmarks, demonstrating effectiveness and generalizability across different LLMs used to execute the generated agents. It also highlights potential for augmenting the reasoning performance of advanced LLMs.