AI/ML

AI/ML

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Large language models, explained with a minimum of math and jargon
Large language models, explained with a minimum of math and jargon

  • 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.

·understandingai.org·
Large language models, explained with a minimum of math and jargon