Keys, Queries, and Values: Understanding Attention Mechanisms in Language Models

Explore the mechanics of attention in Large Language Models through a 52-minute video lecture that draws fascinating parallels between word embeddings and celestial bodies. Delve into how similarity computations enable LLMs like ChatGPT to determine which parts of text deserve focus, visualizing words as planets and stars governed by their own gravitational laws. Master key concepts including similarity measurements, embedding techniques, dot products, cosine similarity, and the crucial roles of Keys, Queries, and Values matrices. Progress through detailed explanations of dimension manipulation, asymmetric relationships, and multi-head attention mechanisms, concluding with a comprehensive overview of how these elements work together in modern language models. Part of a broader LLM educational series, this lecture builds upon foundational concepts while offering a unique astronomical perspective on natural language processing.