Optimizable Model Averaging for Ensembling Heterogeneous Model Outputs
The authors propose Optimizable Model Averaging (OMA) to improve classification performance by learning non-negative weights for heterogeneous foundation models. OMA minimizes regularized predictive entropy, which controls model adaptation. Experiments show OMA outperforms simple output averaging and single-model predictions on image and text classification tasks, demonstrating better performance-calibration trade-offs.