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A new approach called Mixture-of-Agents (MoA) enhances the capabilities of Large Language Models (LLMs) by constructing a layered architecture with multiple agents in each layer. MoA, using open-source LLMs, leads AlpacaEval 2.0 with a score of 65.1%, surpassing GPT-4 Omni's 57.5%.
Iteratively enhanced LLM outputs outperform @OpenAI GPT-4 Omni on AlpacaEval 2.0, MT-Bench, and FLASK! 🤯 Mixture-of-Agents (MoA) uses multiple LLMs in a layered architecture to iteratively enhance the generation quality. Mixture-of-Agents (MoA) 1️⃣ Select multiple LLMs with… https://t.co/BqfH66QwAK
How did a Mixture-of-Agents method Achieves SotA performance and surpassing GPT4o on Alpaca & MT-Bench? Let's have a look at Mixture-of-Agents Enhances Large Language Model Capabilities 👇👇 https://t.co/kS63WP6WeT
Mixture of Agents—a framework that leverages the collective strengths of multiple LLMs. Each layer contains multiple agents that refine responses using outputs from the preceding layer. Together MoA achieves a score of 65.1% on AlpacaEval 2.0. https://t.co/UxA3nrUV5N https://t.co/DNeIHHGoTg
