We also introduce anew on-average stability measure to develop optimistic bounds in a low noise setting.

Because LMs now surpass the performance of most humans except domain experts, evaluating them becomes increasingly challenging.

Generalization to out-of-distribution (OOD) data is one of the central problems in modern machine learning. Recently, there is a surge of attempts to propose algorithms that mainly build upon the idea of extracting invariant features.

Generative Adversarial Networks (GAN) [1] are known for the forefront approach to generating high-fidelity images of diverse categories [2,3,4,5,6,7,8,9].