this post was submitted on 28 Jul 2023
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[โ€“] TheChurn@kbin.social 9 points 2 years ago (2 children)

In the language of classical probability theory: the models learn the probability distribution of words in language from their training data, and then approximate this distribution using their parameters and network structure.

When given a prompt, they then calculate the conditional probabilities of the next word, given the words they have already seen, and sample from that space.

It is a rather simple idea, all of the complexity comes from trying to give the high-dimensional vector operations (that it is doing to calculate conditional probabilities) a human meaning.

[โ€“] pglpm@lemmy.ca 1 points 2 years ago

Superb summary!

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