BERT is just a single text diffusion step

To me, the diffusion-based approach "feels" more akin to whats going on in an animal brain than the token-at-a-time approach of the in-vogue LLMs. Speaking for myself, I don't generate words one a time based on previously spoken words; I start by having some fuzzy idea in my head and the challenge is in serializing it into language coherently.

You 100% do pronounce or write words one at a time sequentially.

But before starting your sentence, you internally formulate the gist of the sentence you're going to say.

Which is exactly what happens in LLMs latent space too before they start outputting the first token.

I'm curious what makes you so confident on this? I confess I expect that people are often far more cognizant of the last thing that the they want to say when they start?

I don't think you do a random walk through the words of a sentence as you conceive it. But it is hard not to think people don't center themes and moods in their mind as they compose their thoughts into sentences.

Similarly, have you ever looked into how actors learn their lines? It is often in a way that is a lot closer to a diffusion than token at a time.

It's just too far of an analogy, it starts in the familiar SWE tarpit of human brain = lim(n matmuls) as n => infinity.

Then, glorifies wrestling in said tarpit: how do people actually compose sentences? Is an LLM thinking or writing? Can you look into how actors memorize lines before responding?

Error beyond the tarpit is, these are all ineffable questions that assume a singular answer to an underspecified question across many bags of sentient meat.

Taking a step back to the start, we're wondering:

Do LLMs plan for token N + X, while purely working to output token N?

TL;DR: yes.

via https://www.anthropic.com/research/tracing-thoughts-language....

Clear quick example they have is, ask it to write a poem, get state at end of line 1, scramble the feature that looks ahead to end of line 2's rhyme.

Let's just not call it planning.

In order to model poetry autoregressively, you're going to need a variable that captures rhyme scheme. At the point where you've ended the first line, the model needs to keep track of the rhyme that was used, just like it does for something like coreference resolution.

I don't think that the mentioned paper shows that the model engages in a preplanning phase in which it plans the rhyme that will come. In fact such would be impossible. Model state is present only in so-far-generated text. It is only after the model has found itself in a poetry generating context and has also selected the first line-ending word, that a rhyme scheme "emerges" as a variable. (Now yes, as you increase the posterior probability of 'being in a poem' given context so far, you would expect that you also increase the probability of the rhyme-scheme variable's existing.)