One important way to visualize what kind of knowledge a language model embodies is to sample from it. Sampling from a distribution means to choose random points sampling according to their likelihood. Thus sampling from a language model-which represents a distribution over sentences-means to generate some sentences, choosing each sentence according to its likelihood as defined by the model. Thus we are more likely to generate sentences that the model thinks have a high probability and less likely to generate sentences that the model thinks have a low probability.