Our goal is to learn a language model p θ based on sentences sampled from the training distribution  When x , classical statistical theory guarantees that a model trained via MLE performs well on the test distribution given sufficient data.
when p test x is not identical to p train x , MLE can perform poorly no matter how much data is observed.