[d3m0t3p]

Do they ? LLM embedd the token sequence N^{L} to R^{LxD}, we have some attention and the output is also R^{LxD}, then we apply a projection to the vocabulary and we get R^{LxV} we get therefore for each token a likelihood over the voc. In the attention, you can have Multi Head attention (or whatever version is fancy: GQA,MLA) and therefore multiple representation, but it is always tied to a token. I would argue that there is no hidden state independant of a token.

Whereas LSTM, or structured state space for example have a state that is updated and not tied to a specific item in the sequence.

I would argue that his text is easily understandable except for the notation of the function, explaining that you can compute a probability based on previous words is understandable by everyone without having to resort to anthropomorphic terminology

[barrkel]

There is hidden state as plain as day merely in the fact that logits for token prediction exist. The selected token doesn't give you information about how probable other tokens were. That information, that state which is recalculated in autoregression, is hidden. It's not exposed. You can't see it in the text produced by the model.

There is plenty of state not visible when an LLM starts a sentence that only becomes somewhat visible when it completes the sentence. The LLM has a plan, if you will, for how the sentence might end, and you don't get to see an instance of that plan unless you run autoregression far enough to get those tokens.

Similarly, it has a plan for paragraphs, for whole responses, for interactive dialogues, plans that include likely responses by the user.

[gpm]

The LLM does not "have" a plan.

Arguably there's reason to believe it comes up with a plan when it is computing token propabilities, but it does not store it between tokens. I.e. it doesn't possess or "have" it. It simply comes up with a plan, emits a token, and entirely throws all its intermediate thoughts (including any plan) to start again from scratch on the next token.

[8note]

this sounds like a fun research area. do LLMs have plans about future tokens?

how do we get 100 tokens of completion, and not just one output layer at a time?

are there papers youve read that you can share that support the hypothesis? vs that the LLM doesnt have ideas about the future tokens when its predicting the next one?