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Recent advancements in large language models (LLMs) have demonstrated significant improvements in natural language understanding and generation tasks. A new approach, termed Mixture-of-Agents (MoA), has been introduced by researchers J Wang, J Wang, B Athiwaratkun, C Zhang, and J Zou from Duke University and TogetherAI. This method constructs a layered architecture where each layer comprises multiple LLM agents, with each agent utilizing outputs from the previous layer to enhance its performance. The MoA approach has achieved state-of-the-art performance on benchmarks such as AlpacaEval 2.0, MT-Bench, and FLASK, surpassing the capabilities of GPT-4 Omni. Notably, MoA, using only open-source LLMs, scored 65.1% on AlpacaEval 2.0, significantly higher than GPT-4 Omni's 57.5%.
Very interesting Paper - "Mixture-of-Agents (MoA) Enhances Large Language Model Capabilities": - MoA using only open-source LLMs is the leader of AlpacaEval 2.0 by a substantial gap, achieving a score of 65.1% compared to 57.5% by GPT-4 Omni. 🔥 📌 The paper introduces the… https://t.co/P09kddjZMt
[CL] Mixture-of-Agents Enhances Large Language Model Capabilities J Wang, J Wang, B Athiwaratkun, C Zhang, J Zou [Duke University & Together AI] (2024) https://t.co/G0MwggzhDt - Recent advances in large language models (LLMs) show great capabilities in language tasks. However,… https://t.co/yKbAHBWJKL
Mixture-of-Agents Enhances Large Language Model Capabilities "In our approach, we construct a layered MoA architecture wherein each layer comprises multiple LLM agents. Each agent takes all the outputs from agents in the previous layer as auxiliary information in generating its… https://t.co/Vo5OvK7NwZ
