Natural Language Processing

Everything above has been about mechanism — how a network is built, how it learns, and what shapes it can take. Natural language processing is the clearest place to watch that mechanism turn into a field, because NLP has been rebuilt from the ground up on each architectural generation in turn.

The lineage runs straight through the architectures in section 4. Recurrent networks and LSTMs were how machines first held a sentence in memory. Transformers and self-attention replaced that memory with direct access to every word at once, and BERT and GPT turned the result into pretrained models the whole field now starts from.

What the architectural story leaves out is the language-specific machinery: how text becomes something a network can accept as input at all, what a word vector encodes, and what a language model is actually estimating. Those have their own article.

Continue with Natural Language Processing — traditional, pre-neural approaches and why they gave way, word embeddings, the transformer architecture in linguistic detail, and the foundational components: tokenization, vector embeddings and language models.

From there, Retrieval Augmented Generation covers what happens when a language model has to work against knowledge it was never trained on.