SMoA: Improving Multi-agent Large Language Models with Sparse Mixture-of-Agents

TL;DR

SMoA introduces a sparse mixture-of-agents framework for multi-agent LLMs, enhancing efficiency and diversity through novel mechanisms, achieving comparable performance to dense models with lower computational costs.

Contribution

The paper proposes a sparse mixture-of-agents framework with Response Selection and Early Stopping, improving multi-agent LLM efficiency and diversity while maintaining performance.

Findings

SMoA achieves similar performance to dense models with less computation.

SMoA is more stable and scalable than traditional approaches.

Hyper-parameter tuning can further enhance SMoA's potential.

Abstract

While multi-agent systems have been shown to significantly enhance the performance of Large Language Models (LLMs) across various tasks and applications, the dense interaction between scaling agents potentially hampers their efficiency and diversity. To address these challenges, we draw inspiration from the sparse mixture-of-agents (SMoE) and propose a sparse mixture-of-agents (SMoA) framework to improve the efficiency and diversity of multi-agent LLMs. Unlike completely connected structures, SMoA introduces novel Response Selection and Early Stopping mechanisms to sparsify information flows among individual LLM agents, striking a balance between performance and efficiency. Additionally, inspired by the expert diversity principle in SMoE frameworks for workload balance between experts, we assign distinct role descriptions to each LLM agent, fostering diverse and divergent thinking. Extensive experiments on reasoning, alignment, and fairness benchmarks demonstrate that SMoA achieves performance comparable to traditional mixture-of-agents approaches but with significantly lower computational costs. Further analysis reveals that SMoA is more stable, has a greater capacity to scale, and offers considerable potential through hyper-parameter optimization. Code and data will be available at: https://github.com/David-Li0406/SMoA.