title: Mixture-of-Agents Enhances Large Language Model Capabilities
summary: 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.
paper_authors._id: 66665d62628a6ad02e599d95
paper_authors.name: Junlin Wang
paper_authors.status: admin_assigned
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paper_authors.fullname: Junlin Wang
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paper_authors.user: junlinw
paper_authors._id: 66665d62628a6ad02e599d96
paper_authors.name: Jue Wang
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paper_authors.fullname: Jue Wang
paper_authors.type: user
paper_authors.user: juewang
paper_authors._id: 66665d62628a6ad02e599d97
paper_authors.name: Ben Athiwaratkun
paper_authors.status: admin_assigned
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paper_authors._id: 64014c531083960bd386ad30
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paper_authors.fullname: Ben Athiwaratkun
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paper_authors.user: benathiwaratkun
paper_authors._id: 66665d62628a6ad02e599d98
paper_authors.name: Ce Zhang
paper_authors.status: admin_assigned
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paper_authors._id: 62f86b5604de855c35e5b050
paper_authors.avatarUrl: /avatars/277ff242cf15b380b80bdabfc0cfa030.svg
paper_authors.fullname: Ce Zhang
paper_authors.type: user
paper_authors.user: zhangce
paper_authors._id: 66665d62628a6ad02e599d99
paper_authors.name: James Zou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-10T10:06:56.181Z
paper_authors._id: 648a769003fc4a3938bb7943
paper_authors.avatarUrl: /avatars/7647f99abdcca4251fcac7783b6fcc8d.svg
paper_authors.fullname: zou
paper_authors.type: user
paper_authors.user: jameszou707
paper_title: Mixture-of-Agents Enhances Large Language Model Capabilities
paper_summary: 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.
paper_ai_keywords: Large Language Models (LLMs) Natural language understanding Natural language generation tasks Mixture-of-Agents (MoA) AlpacaEval 2.0 MT-Bench FLASK ASI (Artificial Superintelligence) Mixture-of-Agents (MoA) AlpacaEval 2.0 MT-Bench FLASK GPT-4 Omni