Comment by sailingparrot
3 months ago
> 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
Autoregressive LLMs don't do that either actually. Sure with one forward pass you only get one token at a time, but looking at what is happening in the latent space there are clear signs of long term planning and reasoning that go beyond just the next token.
So I don't think it's necessarily more or less similar to us than diffusion, we do say one word at a time sequentially, even if we have the bigger picture in mind.
To take a simple example, let’s say we ask an autoregressive model a yes/no factual question like “is 1+1=2?”. Then, we force the LLM to start with the wrong answer “No, “ and continue decoding.
An autoregressive model can’t edit the past. If it happens to sample the wrong first token (or we force it to in this case), there’s no going back. Of course there can be many more complicated lines of thinking as well where backtracking would be nice.
“Reasoning” LLMs tack this on with reasoning tokens. But the issue with this is that the LLM has to attend to every incorrect, irrelevant line of thinking which is at a minimum a waste and likely confusing.
As an analogy, in HN I don’t need to attend to every comment under a post in order to generate my next word. I probably just care about the current thread from my comment up to the OP. Of course a model could learn that relationship but that’s a huge waste of compute.
Text diffusion solves the whole problem entirely by allowing the model to simply revise the “no” to a “yes”. Very simple.
That is precisely what autoregressive means. Perhaps you meant to write that modern LLMs are not strictly autoregressive?
I think they are distinguishing the mechanical process of generation from the way the idea exists. It’s the same as how a person can literally only speak one word at a time but the ideas might be nonlinear.
Indeed what I meant. The LLM isn’t a blank slate at the beginning of each new token during autoregression as the kv cache is there.
If so they are wrong. :) Autoregressive just means that the probability of the next token is just a function of the already seen/emitted tokens. Any "ideas that may exist" are entirely embedded in this sequence.
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If a process is necessary for performing a task, (sufficiently-large) neural networks trained on that task will approximate that process. That doesn't mean they're doing it anything resembling efficiently, or that a different architecture / algorithm wouldn't produce a better result.
I’m not arguing about efficiency though ? Simply saying next token predictors cannot be thought of as actually just thinking about the next token with no long term plan.
They rebuild the "long term plan" anew for every token: there's no guarantee that the reconstructed plan will remain similar between tokens. That's not how planning normally works. (You can find something like this every time there's this kind of gross inefficiency, which is why I gave the general principle.)
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It also doesn’t mean they’re doing it inefficiently.
I read this to mean “just because the process doesn’t match the problem, that doesn’t mean it’s inefficient”. But I think it does mean that. I expect we intuitively know that data structures which match the structure of a problem are more efficient than those that don’t. I think the same thing applies here.
I realize my argument is hand wavey, i haven’t defined “efficient“ (in space? Time? Energy?), and there are other shortcomings, but I feel this is “good enough” to be convincing
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You're right that there is long-term planning going on, but that doesn't contradict the fact that an autoregressive LLM does, in fact, literally generate words one at a time based on previously spoken words. Planning and action are different things.
There is some long term planning going on, but bad luck when sampling the next token can take the process out of rails, so it's not just an implementation detail.