From One-Hot to Contextual: Embeddings Evolution Explained

The progression from one-hot encoding to Word2Vec to transformer-based contextual embeddings represents the most consequential arc in modern NLP. Each generation solved specific limitations of the previous one, and the design principles behind this evolution explain why today's LLMs work the way they do.