Comment by danielmarkbruce
2 days ago
Yup, you are mostly right.
I guess the people in my camp find the "it's just a next token predictor" stupid in that it's like saying "it's just a bunch of carbon and hydrogen", but it's also one of those things where people like to think they are clever because they think they are theoretically correct. But they aren't even that. So it's like double stupid. But the "next token predictor" part is at least technically correct (like, carbon and hydrogen right) for pretraining, so the debate can't really be won there.
It really is an important distinction, though. Being a next token predictor doesn't stop it from writing good sentences, but it does mean an LLM by itself can't play the number guessing game with you.
This is pedantic, but, actually RL has improved the quality of sentence construction in LLMs quite dramatically... And once you do some RL on that model, it aint a next token prediction machine any longer.
That's just training, isn't it? (Even if the term is "post-training") It doesn't change how inference works.
My understanding is it usually strengthens the "thinking ahead" part of inference, but that part was already there, and it's still at the end of the day picking one token and then purging internal state in a way that can only partially be recovered from.
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they are cleverly arranged / configured next-most-likely-token predictors, possibly with some clever procedures / attachments on top.
Nope. This isn't right.
> "it's not just a next-token predictor because a bunch of the training isn't about predicting the next token."
clever procedures on top of the base transformer architecture.
i used simplified words/phrases to summarise the same thing you two were saying (the intent being: here's a version that may be digestible when discussing with others).
apparently that means i'm wrong though, no idea why because it seems you've decided to be dismissive rather than constructively elaborate on why this simplified and digestible version might be wrong :shrug:
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