Mixture of Agents
An approach where multiple LLMs collaborate on a task, with each model contributing its strengths and a combining mechanism selecting or merging the best outputs.
How It Works
Multiple models (possibly different architectures or sizes) independently process the same input. Their outputs are then combined through voting, ranking, or a meta-model that selects the best response.
Benefits
Leverages diverse model strengths, reduces individual model weaknesses, improves reliability through redundancy, and can outperform any single model on diverse tasks.
Practical Implementations
Router-based systems that direct queries to the best-suited model. Debate protocols where models critique each other. Ensemble approaches that merge multiple model outputs.