How large language models (LLMs) like GPT-3 work:

– LLMs are trained on massive datasets of text data, often from the internet. This can include books, Wikipedia, news articles, blogs, and more.

– The model learns the statistical patterns and relationships between words in this huge dataset. It builds an understanding of the syntax, semantics, and context of human language.

– LLMs use an artificial neural network architecture. This contains an encoder and a decoder.

– The encoder takes in an input text prompt and converts it into an embedded numerical representation.

– This embedded input passes through multiple layers in the neural network. Each layer looks at the text from a different perspective and learns different aspects of the language.

– The decoder takes this encoded representation and generates predicted output text. It predicts the most likely next word in a sequence given the context.

– Generation is done word-by-word. As the model generates each word, it is fed back into the model to predict the next word. This continues until the output is complete.

– During training, the weights and parameters of the neural network are tuned through an optimization process called backpropagation and gradient descent. This allows the model to improve its predictions over many iterations.

– Once trained, the model can generate coherent, human-like text for a given prompt, answering questions, summarizing texts, and more. The scale and depth of the pre-training is what gives LLMs their expressive power.