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Background

  • Background The paper presents that Large Language Models (LLMs) have made significant advances in natural language understanding and generation tasks in recent years. Diversity in skill sets among different LLMs raises the question if their collective expertise can be harnessed to create a more capable and robust model.

  • Existing Work Despite the impressive achievements of individual LLMs, they face inherent constraints in model size and training data. Scaling up these models further is exceptionally costly, often requiring extensive retraining on several trillion tokens.

Core Contributions

  • Introduces a new method

    • Challenge 1: Enhance generation quality The paper introduces a new Mixture-of-Agents (MoA) methodology that leverages multiple LLMs iteratively to enhance the generation quality. Each layer in MoA includes multiple LLM agents using outputs from previous layer agents as auxiliary information to generate their responses, achieving state-of-the-art performance on AlpacaEval 2.0, MT-Bench, and FLASK surpassing GPT-4 Omni.

    • Challenge 2: Ensure effective collaboration among models To ensure effective collaboration among models and improve the overall response quality, careful selection of LLMs for each MoA layer is essential. The selection process guided by two primary criteria: performance metrics and diversity considerations aims to mitigate individual model deficiencies and enhance overall response quality through collaborative synthesis.

Implementation and Deployment

Comprehensive evaluations were conducted using competitive benchmarks like AlpacaEval 2.0, MT-Bench, and FLASK. The results demonstrate substantial improvements with the new method, achieving a score of 65.1% on AlpacaEval 2.0 using only open-source LLMs compared to 57.5% by GPT-4 Omni and a new SOTA win rate of 65.8%.

Summary

This paper showcases the Mixture-of-Agents (MoA) methodology for enhancing the capabilities of LLMs in understanding and generating natural language by leveraging the group expertise of multiple models. Through experimentation, the method has been validated to significantly improve performance, achieving state-of-the-art results on multiple competitive benchmarks.