Summary: The article explores the inner workings of large language models (LLMs) like ChatGPT. It begins by discussing word vectors, numerical representations of words in high-dimensional spaces, and how LLMs use them to capture word meanings and relationships. It then delves into the transformer architecture, the core component of LLMs, which processes word vectors through multiple layers to understand context and predict the next word in a sentence. Despite the complexity, LLMs aim to generate coherent text by gradually refining their understanding through numerous layers.
