Enhancing Large Foundation Models with a Principled Temperature Prediction Network

A principled framework for learning a small yet generalizable temperature prediction network (TempNet) to improve the performance of large foundation models, such as large language models and CLIP models, by optimizing a robust loss underpinned by constrained distributionally robust optimization.