Multi-Objective Orchestration of Small Language Model Ensembles: Balancing Accuracy, Diversity, and Fairness
The method combines an interpretable cost-function formulation with a multi-objective evolutionary algorithm to discover Pareto-optimal ensemble configurations and introduces a two-stage combiner that produces diverse candidate responses and selects final outputs via embedding-based semantic consensus.