Generative models for language are artificial intelligence systems designed to create new text that resembles human writing. Unlike discriminative models that classify or predict categories, generative models learn the underlying probability distributions of language to produce novel and coherent text outputs.
Several neural network architectures are used for language generation. Recurrent Neural Networks process text sequentially, while Long Short-Term Memory networks can handle longer dependencies. However, Transformer architectures have become dominant because they use attention mechanisms to process sequences in parallel and capture long-range relationships more effectively.
The training process teaches the model to predict the next word in a sequence. During training, the model receives text sequences and learns to calculate the probability of each possible next token. The loss function, typically cross-entropy, measures how well the model's predictions match the actual next words. Through iterative updates, the model learns statistical patterns and relationships in language.