From the source
This module explores language models, which estimate the probability of a token or sequence of tokens occurring within a longer sequence, enabling tasks like text generation, translation, and summarization.
Language models utilize context, the surrounding information of a target token, to enhance prediction accuracy, with recurrent neural networks offering more context than traditional N-grams.
N-grams are ordered sequences of words used to build language models, with longer N-grams providing more context but potentially encountering sparsity issues.