I think the snag is that people use "Next token predictor" as a dismissive statement. In reality it's about as functionally dismissive as "humans only communicate one word at a time".

Emitting and predicting are different things though. Prediction implies there is some "truth" or event or something that you can test against. Prediction implies the model just learns from existing text, and optimizes to predict the next token in training data. That's just not true.

> What is true, however, is that in order to generate the next token autoregressive LLMs produce internal/hidden state about future tokens far past the next token so that it's not like the entire machinery of the algorithm deprives itself of representing where the sentence/text is headed.

If we suppose that the word "know" can sanely be applied to LLMs at all, then "A LLM already knows more tokens than the current one." seems to me like a perfectly reasonable restatement of that, and not any kind of misinterpretation.

> There's no revision or means of correction, and sometimes this can lead an LLM to route around its own earlier mistakes or simply produce false statements/hallucinations instead of going back and fixing them.

Yes. There is no contradiction. Similarly, when humans speak, we surely have in mind the next few words we're going to say (or at least partial information about them), and may not realize the fault in them until after hearing ourselves utter them. But LLMs are not trained to output "excuse me, I mean…" sorts of things, because they're expected to output primarily as text (which might possibly then be fed to TTS).

Aren't we still using a definition that applies to humans, though? If I'm speaking to you I can't change what was already said. Even if I'm typing something, I'm producing new tokens (backspace) to fix what was output, just as an LLM would with its harness.