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

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.

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Chapters:
0:00 Introduction
0:53 Mixture-of-Agents (MoA)
2:40 Results

[RobBrogan]

A little bit like how I’m using Perplexity that lets me refresh a response with a different model. Except I’m using my human brain to choose an ideal model or draw info from different ones.

Or how back in the day, the best search engine was this one that used multiple services (dogpile? Can’t remember).

Maybe my comparison is bad, but definitely look forward to a tool that combines multiple LLMs.

[OpenAITutor]

I love this approach. I created a version using groq and open-webui ! It rocks !!