Researchers Derive Predictive Law for Diversity-Driven Performance Gains in LLM Ensembles
A new preprint introduces and experimentally validates a formal law that predicts how diversity among large language models (LLMs) in an ensemble contributes to improved performance. The authors propose an accuracy-adjusted correctness correlation metric and demonstrate its predictive power across 767,520 inferences from ten models on three benchmarks, achieving Spearman's ρ up to 0.84 for ensemble lift prediction.
Why it matters: This work offers a principled, quantitative framework for optimizing LLM ensemble design by enabling practitioners to predict and maximize the benefits of model diversity.
Full story at: arXiv Multiagent Systems ↗