Original Paper: https://arxiv.org/abs/2406.04692
By: Junlin Wang, Jue Wang, Ben Athiwaratkun, Ce Zhang, James Zou
Abstract:
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 expertise of multiple LLMs is an exciting open direction. Toward this goal, we propose a new approach that leverages the collective strengths of multiple LLMs through a Mixture-of-Agents (MoA) methodology. 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 response. MoA models achieves state-of-art performance on AlpacaEval 2.0, MT-Bench and FLASK, surpassing GPT-4 Omni. For example, our 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.
In the ever-evolving field of natural language processing (NLP), large language models (LLMs) have shown remarkable capabilities in understanding and generating human language. However, despite their impressive achievements, these models often face limitations in terms of cost and specialization. The latest research from Duke University and Together AI introduces a novel methodology called Mixture-of-Agents (MoA), which leverages the strengths of multiple LLMs to create a more efficient and robust model.
Imagine a team of experts, each specializing in different areas, working together to solve a complex problem. This is the essence of the Mixture-of-Agents (MoA) approach. The research explores how multiple LLMs can collaborate to enhance the overall performance, making it a promising direction for future advancements in NLP.
Recent advances in LLMs, such as GPT-4, have demonstrated substantial capabilities in various NLP tasks. However, these models are not without their challenges. They are exceptionally costly to train and maintain, requiring extensive retraining on vast amounts of data. Furthermore, individual models often excel in specific tasks but may not perform well in others. For instance, some models are better at following complex instructions, while others excel in code generation.
This diversity in skill sets among different LLMs raises an intriguing question: Can we harness the collective expertise of multiple LLMs to create a more capable and robust model?