Understanding Vectors and Transformer Architecture in Language Models

In machine learning, vectors function as sequenced numerical coordinates that map abstract concepts into a multi-dimensional mathematical landscape. By converting human language into word embeddings, neural networks can translate semantic concepts and relationships into measurable geometric distances. Because traditional static definitions fail to capture ambiguous terms, Transformer architecture advances this concept further by utilizing position markers and dynamic calculations. Through the self-attention mechanism, these models evaluate Query, Key, and Value vectors to determine contextual relevance between words simultaneously. Ultimately, this mathematical steering allows language models to continuously adjust a word's position in coordinate space to reflect its exact meaning within a specific sentence.