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Mixture-of-Agents Enhances Large Language Model Capabilities
Metric MoA Model Performance
AlpacaEval 2.0 65.1%
MT-Bench Top-performance
FLASK Exceeds GPT-4 Omni

Recent advances in LLMs have shown significant capabilities in natural language tasks. The MoA approach constructs a layered architecture with LLM agents, achieving top performance on AlpacaEval 2.0 and other benchmarks. This research highlights the power of collective LLM expertise in enhancing AI capabilities and sets a new benchmark for future models.

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