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“Next-token predictor” is the wrong mental model for LLMs

gmcgoldr.github.io

341–343 of 343 posts

Re: “Next-token predictor” is the wrong mental model for LLMs

#341

Earlier quoted context omitted.

During training, certain tokens are more likely to lead to a lower loss function value, which is how you "win" the game of LLM output.

So, next-token predictors

I keep seeing this stochastically parroted here. Perhaps by humans but how knows?

Re: “Next-token predictor” is the wrong mental model for LLMs

#342
post #4

yep "next-embedding" predictor is more correct, and not just at the end but through the layers, and folding back dimensions into that one next token is one small final step, and next-embedding could be named "next-meaning" as well, and we're getting there... this sentence above would made a longer article if I bothered to so blog as is being blogged here

Exactly. There's a widespread misconception that it works on tokens all the way through. Tokens are only at the input and output edges. All the internal transformation is in the many-dimensional tensors variously described as "magic" or "not magic" or "black box", or hand-waved away as "various mathematical operations".
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