Word Vectorization is a technique used by large language models (LLMs) to learn how to write like humans. It involves transforming words into numbers, or vectors, which represent the relationships between words based on their frequency of co-appearance in documents. These word vectors can be manipulated using mathematical operations such as addition and subtraction, allowing LLMs to understand context and complete sentences. Models like BERT and GPT-3 utilize this technique to generate human-like text by processing large amounts of data during training.