What Is ChatGPT Doing ... and Why Does It Work? by Stephen Wolfram

Author:Stephen Wolfram [Wolfram, Stephen]
Language: eng
Format: azw3, epub, mobi
Publisher: Wolfram Media, Inc.
Published: 2023-03-10T00:00:00+00:00

The input is a vector of n tokens (represented as in the previous section by integers from 1 to about 50,000). Each of these tokens is converted (by a single-layer neural net ) into an embedding vector (of length 768 for GPT-2 and 12,288 for ChatGPT’s GPT-3). Meanwhile, there’s a “secondary pathway” that takes the sequence of (integer) positions for the tokens, and from these integers creates another embedding vector. And finally the embedding vectors from the token value and the token position are added together —to produce the final sequence of embedding vectors from the embedding module.

Why does one just add the token-value and token-position embedding vectors together? I don’t think there’s any particular science to this. It’s just that various different things have been tried, and this is one that seems to work. And it’s part of the lore of neural nets that—in some sense—so long as the setup one has is “roughly right” it’s usually possible to home in on details just by doing sufficient training, without ever really needing to “understand at an engineering level” quite how the neural net has ended up configuring itself.

Here’s what the embedding module does, operating on the string hello hello hello hello hello hello hello hello hello hello bye bye bye bye bye bye bye bye bye bye :

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