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
TogetherAI presents Mixture-of-Agents Enhances Large Language Model Capabilities Achieves SotA performance on AlpacaEval 2.0, MT-Bench and FLASK, surpassing GPT4o https://t.co/RTXgstlgKA https://t.co/d3ghIH41fi
Mixture-of-Agents Enhances Large Language Model Capabilities Recent advances in large language models (LLMs) demonstrate substantial capabilities in natural language understanding and generation tasks. With the growing number of LLMs, how to harness the collective https://t.co/mHYBh5vYur