Artificial intelligence systems can now write articles, explain scientific concepts, generate software code, summarize documents, and participate in remarkably natural conversations. At the center of this development is a class of language models commonly associated with three letters: GPT.
Despite its widespread use, GPT is often described too simply. It is not merely a chatbot, a search engine, or a database containing prepared answers. GPT is a neural language model trained to process sequences of tokens and predict what should come next.
Understanding GPT therefore requires looking beyond the chat interface. We need to examine tokenization, Transformer architecture, pre-training, parameters, post-training, and the statistical process through which a model produces language.
What Does GPT Stand For?
GPT stands for Generative Pre-trained Transformer. Each word describes a fundamental part of the system.
