Large language models like GPT-3 work by representing words as vectors of numbers and using neural networks with attention and transformer layers.

Word vectors allow language models to perform operations and reason about words in ways that strings of letters cannot.

Attention heads allow words to share contextual information with each other, helping the model resolve ambiguities and predict the next word.

Feed-forward layers act as a database of facts that the model has learned, enabling it to make predictions based on that knowledge.

Language models are trained by trying to predict the next word in text, requiring huge amounts of training data.

The performance of language models scales dramatically with their size, the amount of training data, and the compute used for training.

As language models get larger, they develop the ability to perform more complex reasoning and tasks requiring abstract thought.

Researchers do not fully understand how language models accomplish their abilities, and fully explaining them remains a huge challenge.

Language models appear to spontaneously develop capabilities like theory of mind as a byproduct of increasing language ability.

There is debate over whether language models truly "understand" language in the same sense that humans do.