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
“Next-token predictor” is the wrong mental model for LLMs
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Re: “Next-token predictor” is the wrong mental model for LLMs
#342yep "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
Re: “Next-token predictor” is the wrong mental model for LLMs
#343Re: “Next-token predictor” is the wrong mental model for LLMs
#344The biggest problem is the word "predictor". Once you get into post training with RLHF and RLVR, it simply isn't doing that. It is not predicting anything. It's producing tokens, but it isn't predicting them. The chess analogy in the post is a good one - it's closer to searching for a set of moves that give a result than predict. It's search for a set of ideas, represented as locations in very high dimensional space,…
"Guessing" is more accurate than "predicting". It takes educated guesses.