Scaling Large-Language-Model-based Multi-Agent Collaboration
arxiv(2024)
摘要
Pioneering advancements in large language model-powered agents have
underscored the design pattern of multi-agent collaboration, demonstrating that
collective intelligence can surpass the capabilities of each individual.
Inspired by the neural scaling law, which posits that increasing neurons leads
to emergent abilities, this study investigates whether a similar principle
applies to increasing agents in multi-agent collaboration. Technically, we
propose multi-agent collaboration networks (MacNet), which utilize directed
acyclic graphs to organize agents and streamline their interactive reasoning
via topological ordering, with solutions derived from their dialogues.
Extensive experiments show that MacNet consistently outperforms baseline
models, enabling effective agent collaboration across various network
topologies and supporting cooperation among more than a thousand agents.
Notably, we observed a small-world collaboration phenomenon, where topologies
resembling small-world properties achieved superior performance. Additionally,
we identified a collaborative scaling law, indicating that normalized solution
quality follows a logistic growth pattern as scaling agents, with collaborative
emergence occurring much earlier than previously observed instances of neural
emergence. The code and data will be available at
https://github.com/OpenBMB/ChatDev.
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