Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models
  Beneficial?
Ensembling outputs from diverse sources is a straightforward yet effective
approach to boost performance. Mixture-of-Agents (MoA) is one such popular
ensemble method that aggregates outputs from multiple different Large Language
Models (LLMs). This paper raises the question in the context of language
models: is mixing different LLMs truly beneficial? We propose Self-MoA -- an
ensemble method that aggregates outputs from only the single top-performing
LLM. Our extensive experiments reveal that, surprisingly, Self-MoA outperforms
standard MoA that mixes different LLMs in a large number of scenarios: Self-MoA
achieves 6.6% improvement over MoA on the AlpacaEval 2.0 benchmark, and an
average of 3.8% improvement across various benchmarks, including MMLU, CRUX,
and MATH. Applying Self-MoA to one of the top-ranking models in AlpacaEval 2.0
directly achieves the new state-of-the-art performance on the leaderboard. To
understand the effectiveness of Self-MoA, we systematically investigate the
trade-off between diversity and quality of outputs under various MoA settings.
We confirm that the MoA performance is rather sensitive to the quality, and
mixing different LLMs often lowers the average quality of the models. To
complement the study, we identify the scenarios where mixing different LLMs
could be helpful. This paper further introduces a sequential version of
Self-MoA, that is capable of aggregating a large number of LLM outputs
on-the-fly over multiple rounds, and is as effective as aggregating all outputs
at once.
67a362c9b9a2bb11fdba4b9f
Wenzhe Li
67a362c9b9a2bb11fdba4ba0
Yong Lin
67a362c9b9a2bb11fdba4ba1
Mengzhou Xia
67a362c9b9a2bb11fdba4ba2
Chi Jin
Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models
  Beneficial?
Ensembling outputs from diverse sources is a straightforward yet effective
approach to boost performance. Mixture-of-Agents (MoA) is one such popular
ensemble method that aggregates outputs from multiple different Large Language
Models (LLMs). This paper raises the question in the context of language
models: is mixing different LLMs truly beneficial? We propose Self-MoA -- an
ensemble method that aggregates outputs from only the single top-performing
LLM. Our extensive experiments reveal that, surprisingly, Self-MoA outperforms
standard MoA that mixes different LLMs in a large number of scenarios: Self-MoA
achieves 6.6% improvement over MoA on the AlpacaEval 2.0 benchmark, and an
average of 3.8% improvement across various benchmarks, including MMLU, CRUX,
and MATH. Applying Self-MoA to one of the top-ranking models in AlpacaEval 2.0
directly achieves the new state-of-the-art performance on the leaderboard. To
understand the effectiveness of Self-MoA, we systematically investigate the
trade-off between diversity and quality of outputs under various MoA settings.
We confirm that the MoA performance is rather sensitive to the quality, and
mixing different LLMs often lowers the average quality of the models. To
complement the study, we identify the scenarios where mixing different LLMs
could be helpful. This paper further introduces a sequential version of
Self-MoA, that is capable of aggregating a large number of LLM outputs
on-the-fly over multiple rounds, and is as effective as aggregating all outputs
at once.
Self-MoA, an ensemble method using outputs from a single top-performing LLM, outperforms standard MoA that aggregates multiple LLMs across various benchmarks.
Mixture-of-Agents Large Language Models AlpacaEval 2.0 MMLU CRUX MATH sequential version of Self-MoA