In this chapter, we'll begin exploring the RNN and transformer architectures through the lens of probabilistic language models, so let's briefly remind ourselves of the framework for language modeling. Recall from Chapter 3 that probabilistic language models predict the next word in a sequence given some preceding context. For example, if the preceding context is "Thanks for all the" and we want to know how likely the next word is "fish" we would compute: