Comment by taeric
3 months ago
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.
I think there is a wide range of ways to "turn something in the head into words", and sometimes you use the "this is the final point, work towards it" approach and sometimes you use the "not sure what will happen, lets just start talking and go wherever". Different approaches have different tradeoffs, and of course different people have different defaults.
I can confess to not always knowing where I'll end up when I start talking. Similarly, not every time I open my mouth it's just to start but sometimes I do have a goal and conclusion.
They're speaking literally. When talking to someone (or writing), you ultimately say the words in order (edits or corrections notwithstanding). If you look at the gifs of how the text is generated - I don't know of anyone that has ever written like that. Literally writing disconnected individual words of the actual draft ("during," "and," "the") in the middle of a sentence and then coming back and filling in the rest. Even speaking like that would be incredibly difficult.
Which is not to say that it's wrong or a bad approach. And I get why people are feeling a connection to the "diffusive" style. But, at the end of the day, all of these methods do build as their ultimate goal a coherent sequence of words that follow one after the other. It's just a difference of how much insight you have into the process.
Weird anecdote, but one of the reasons I have always struggled with writing is precisely that my process seems highly nonlinear. I start with a disjoint mind map of ideas I want to get out, often just single words, and need to somehow cohere that into text, which often happens out-of-order. The original notes are often completely unordered diffusion-like scrawling, the difference being I have less idea what final the positions of the words were going to be when I wrote them.
I can believe that your abstract thoughts in latent space are diffusing/forming progressively when you are thinking.
But I can't believe the actual literal words are diffusing when you're thinking.
When being asked: "How are you today", there is no way that your thoughts are literally like "Alpha zulu banana" => "I banana coco" => "I banana good" => "I am good". The diffusion does not happen at the output token layer, it happens much earlier at a higher level of abstraction.
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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.)
I’m confused: the blog shows they A) predict the end of line 2 using the state at the end of line 1 and B) can choose the end of line 2 by altering state at end of line 1.
Might I trouble you for help getting from there to “such would be impossible”, where such is “the model…plans the rhyme to come”
Edit: I’m surprised to be at -2 for this. I am representing the contents of the post accurately. Its unintuitive for sure, but, it’s the case.
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> Model state is present only in so-far-generated text
Wrong. There's "model state", (I assume you mean hidden layers), not just in the generated text, but also in the initial prompt given to the model. I.e. the model can start its planning from the moment it's given the instruction, without even having predicted a token yet. That's actually what they show in the paper above...
> 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
This is an assertion based on flawed reasoning.
(Also, these ideas should really be backed up by evidence and experimentation before asserting them so definitively.)
> far more cognizant of the last thing that the they want to say when they start
This can be captured by generating reasoning tokens (outputting some representation the desired conclusion in token form, then using it as context for the actual tokens), or even by an intermediate layer of a model not using reasoning.
If a certain set of nodes are strong contributors to generate the concluding sentence, and they remain strong throughout all generated tokens, who's to say if those nodes weren't capturing a latent representation of the "crux" of the answer before any tokens were generated?
(This is also in the context of the LLM being able to use long-range attention to not need to encode in full detail what it "wants to say" - just the parts of the original input text that it is focusing on over time.)
Of course, this doesn't mean that this is the optimal way to build coherent and well-reasoned answers, nor have we found an architecture that allows us to reliably understand what is going on! But the mechanics for what you describe certainly can arise in non-diffusion LLM architectures.
It must be the case that some smart people have studied how we think, right?
The first person experience of having a thought, to me, feels like I have the whole thought in my head, and then I imagine expressing it to somebody one word at a time. But it really feels like I’m reading out the existing thought.
Then, if I’m thinking hard, I go around a bit and argue against the thought that was expressed in my head (either because it is not a perfect representation of the actual underlying thought, or maybe because it turns out that thought was incorrect once I expressed it sequentially).
At least that’s what I think thinking feels like. But, I am just a guy thinking about my brain. Surely philosophers of the mind or something have queried this stuff with more rigor.
People don't come up with things their brain does.
Words rise from an abyss and are served to you, you have zero insight into their formation. If I tell you to think of an animal, one just appears in your "context", how it got there is unknown.
So really there is no argument to be made, because we still don't mechanistically understand how the brain works.
We don't know exactly how consciousness works in the human brain, but we know way more than "comes from the abyss".
When I read that text, something like this happens:
Visual perception of text (V1, VWFA) → Linguistic comprehension (Angular & Temporal Language Areas) → Semantic activation (Temporal + Hippocampal Network) → Competitive attractor stabilization (Prefrontal & Cingulate) → Top-down visual reactivation (Occipital & Fusiform) → Conscious imagery (Prefrontal–Parietal–Thalamic Loop).
and you can find experts in each of those areas who understand the specifics a lot more.
aeonik says >"We don't know exactly how consciousness works in the human brain, but we know way more than "comes from the abyss"."<
You are undoubtedly technically correct, but I prefer the simplicity, purity and ease-of-use of the abysmal model, especially in comparison with other similar competing models, such as the below-discussed "tarpit" model.