What Is ChatGPT Doing ... And Why Does It Work?

and what’s a dog, and then have the network “machine learn” from these how to distinguish them. And the point is that the trained network “generalizes” from the particular examples it’s shown. Just as we’ve seen above, it isn’t simply that the network recognizes the particular pixel pattern of an example cat image it was shown; rather it’s that the

The basic concept of ChatGPT is at some level rather simple. Start from a huge sample of human-created text from the web, books, etc. Then train a neural net to generate text that’s “like this”. And in particular, make it able to start from a “prompt” and then continue with text that’s “like what it’s been trained with”.

But this is where a bit of voodoo begins to creep in. Because for some reason—that maybe one day we’ll have a scientific-style understanding of—if we always pick the highest-ranked word, we’ll typically get a very “flat” essay, that never seems to “show any creativity” (and even sometimes repeats word for word). But if sometimes (at random) we pick

It operates in three basic stages. First, it takes the sequence of tokens that corresponds to the text so far, and finds an embedding (i.e. an array of numbers) that represents these. Then it operates on this embedding—in a “standard neural net way”, with values “rippling through” successive layers in a network—to

OK, so after the embedding module comes the “main event” of the transformer: a sequence of so-called “attention blocks”

And a key idea in the construction of ChatGPT was to have another step after “passively reading” things like the web: to have actual humans actively interact with ChatGPT, see what it produces, and in effect give it feedback on “how to be a good chatbot”.

And one way to do this is just to assign a unique number to each of the 50,000 or so common words in English. So, for example, “the” might be 914, and “cat” (with a space before it) might be 3542. (And these are the actual numbers used by GPT-2.) So for the “the ___ cat” problem, our input might be {914, 3542}. What should the output be like?

But even within the framework of existing neural nets there’s currently a crucial limitation: neural net training as

and that we can think of as “characterizing each word”.