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 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.
66665d62628a6ad02e599d95
Junlin Wang
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2024-06-10T10:03:51.141Z
64b6ceae33c80d14a2033b94
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Junlin Wang
user
junlinw
66665d62628a6ad02e599d96
Jue Wang
admin_assigned
2024-06-10T10:05:14.026Z
61dce5c2af6d5e733e0fb08b
https://cdn-avatars.huggingface.co/v1/production/uploads/1669380535028-61dce5c2af6d5e733e0fb08b.jpeg
Jue Wang
user
juewang
66665d62628a6ad02e599d97
Ben Athiwaratkun
admin_assigned
2024-06-10T10:05:44.975Z
64014c531083960bd386ad30
/avatars/6444fb06ca1f70bad601e07b4292c643.svg
Ben Athiwaratkun
user
benathiwaratkun
66665d62628a6ad02e599d98
Ce Zhang
admin_assigned
2024-06-10T10:07:51.433Z
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/avatars/277ff242cf15b380b80bdabfc0cfa030.svg
Ce Zhang
user
zhangce
66665d62628a6ad02e599d99
James Zou
admin_assigned
2024-06-10T10:06:56.181Z
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zou
user
jameszou707
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 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.
A Mixture-of-Agents (MoA) methodology combines multiple large language models (LLMs) to achieve state-of-the-art performance on various evaluations.
Mixture-of-Agents MoA large language models LLMs AlpacaEval 2.0 MT-Bench FLASK GPT-4 Omni