A new paper titled "Mixture-of-Agents Enhances Large Language Model Capabilities" shows a method to win GPT-4o on AlpacaEval 2.0 using open-source large language models (LLMs).
In this video we explain what is the Mixture-of-Agents (MoA) method by diving into that research paper.

Mixture-of-Agents (MoA) is inspired by the well-known Mixture-of-Experts (MoE) method, but unlike MoE, which embeds the experts in different model segments of the same LLM, MoA is using full-fledged LLMs as the different experts.

Paper page - https://arxiv.org/abs/2406.04692
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Chapters:
0:00 Introduction
0:53 Mixture-of-Agents (MoA)
2:40 Results

AI Papers Academy

Mixture of Agents (MoA) is a method that enables multiple LLMs to collaborate in a layered architecture to produce better responses than any single model. Each layer passes outputs from multiple LLMs to the next, with a final aggregator model synthesizing all intermediate responses using a special prompt. The approach achieved state-of-the-art results on AlpacaEval 2, surpassing GPT-4 by a significant margin. Results show that most models benefit from seeing responses from other models, though the degree of improvement varies.

Mixture-of-Agents (MoA) Enhances Large Language Model Capabilities