Mixture of Agents (MOA) Outperforms GPT-4.0 with Open-Source Models

In this video, Matthew Berman discusses a new paper and code release from TogetherAI that introduces the Mixture of Agents (MOA) approach. This method allows multiple large language models (LLMs) to work together as agents to produce superior outputs, outperforming even GPT-4.0. The video explains the concept of MOA, which leverages the collective intelligence of various open-source models to improve the quality of responses through collaboration.

The MOA framework involves multiple layers of agents, where each layer consists of different models working together. These agents can share the same model or use different models at each layer. The process begins with proposers generating initial responses, which are then synthesized by aggregators into higher-quality responses. This iterative process continues through several layers until a robust and comprehensive response is achieved.

The video highlights the significant performance gains achieved by MOA on the alpaca eval 2.0 benchmark, surpassing GPT-4.0 by a notable margin. However, the approach does come with a trade-off in terms of slower time to the first token. Berman suggests that integrating technologies like Groq could potentially address this latency issue.

The video also includes a practical demonstration of the MOA framework, where Berman tests it using various open-source models. The results show that the collaborative approach of MOA leads to highly accurate and coherent outputs, even for complex tasks that typically challenge individual models.

Overall, the video emphasizes the potential of agentic frameworks and collaborative intelligence in advancing the capabilities of LLMs, making them more efficient, cost-effective, and versatile.