2026
Rethinking LLM Ensembling from the Perspective of Mixture Models
ICML 2026spotlight
Model ensembling is a well-established technique for improving the performance of machine learning models. Conventionally, this involves averaging the output distributions of multiple models and selecting the most probable label. This idea has been naturally extended to large language models (LLMs),…