Mixture-of-Agents Enhances Large Language Model Capabilities

[Andrew]

Recent research introduces a Mixture-of-Agents (MoA) methodology to enhance large language models (LLMs). This approach leverages multiple LLMs in a layered architecture, where each agent utilizes the outputs of previous layers to generate responses. MoA has demonstrated superior performance on various benchmarks, including AlpacaEval 2.0 and MT-Bench, surpassing even GPT-4 Omni. For instance, using solely open-source LLMs, MoA leads AlpacaEval 2.0 with a significant score difference (65.1% vs. 57.5%).

 

 

For further details, refer to the full paper: Mixture-of-Agents Enhances Large Language Model Capabilities.