Comment by atleastoptimal
2 days ago
"Next-token predictor" is one of those phrases used most of the time with a motive to downplay the abilities and faculties of AI models. It is intended to trivialize LLM's and imply that there is some fundamental limit on their capacities.
Relying on it as a mental model for what LLM's are minimizes the emergent properties of scaling. It's like imagining that unicellular life could never eventually evolve into complex multi-cellular organisms because individual cells are just "survival and next-mitosis optimizers"
At the same time, it ... is literally a next token predictor. Like that's what it is. The input is a sequence of tokens. The output is a probability distribution of next tokens.
This comment attracted a lot of analogies trying to reduce something to something else (calling humans a "bag of chemicals"), but the flaw in those analogies is that they're reducing something valuable to something that sounds less valuable.
With an LLM, the tokens are the valuable part. That's what I want from it. That's why it exists. The tokens are the point, and it produces those tokens one by one for me.
Simple Markov chains are next token predictors, and they can provide you with much more tokens than you can consume, and much cheaper than from llms. Unbeatable in price and simplicity.
But there is no trillion dollar industry around cheap top Markov models. So there must be something about LLM tokens that makes them more valuable than those generated from a simple Markov chain. And that substance, that makes one valuable and the other not, is exactly what reduction to "next-token predictors" masks.
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You are not wrong, but I think atleastoptimal's suggestion was that descriptions like "next-token predictor" are often used to imply that there's not much to see here, perhaps with an implied "obviously" in there. "Stochastic parrot" seems to be a case in point: no-one expects intelligent, informed conversation or working software from a parrot - not even the eternally-resting Alex.
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It is. And human beings are bags of chemicals. But for many purposes you will not find it helpful to think of human beings as bags of chemicals, and for many purposes you will not find it helpful to think of LLMs as next-token predictors.
> But for many purposes you will not find it helpful to think of human beings as bags of chemicals
But when we talk about humans, we're not talking about the chemicals involved in those humans.
When we talk about LLMs, the tokens are the valuable thing they produce for us. We want LLMs because they give us sequences of tokens.
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It can be pretty helpful to think of human function in chemical terms. Its at least unhelpful to deny it.
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I think a better simplistic analogy would be that humans are progeny-maximizers. Optimization problems can give rise to all sorts of interesting behaviors but the simplistic perspective is also useful and interesting in both cases
Humans are next state of their local world predictors, given all previous states they are aware of. That's an entirely fair analogy. The reverse analogy for calling a human a bag of chemicals would be calling an LLM a sequence of bytes loaded from disk to memory, the most reductive possible description of any piece of software at all.
To be clear, all life is a next state of the local world predictor. What makes humans somewhat unique among life is we're much better at predicting states of the world neither we nor any of our ancestors have ever experienced, for various reasons such as having the ability to legibly communicate very complicated information strings to each other, being able to build and use tools to record states of the world we can't directly sense.
Similarly, what makes LLMs and multimodal versions of the same architectures "better" than previous generations of electronic predictive models is factors like being able to read and understand roughly the same corpus of data humans have been recording all these millennia, being able to read and remember much more of it than any individual human, and being better at generalizing than other electronic predictive models, but not better than humans. And, of course, they can produce far more predictions in far less time. Frankly, that is probably the key advantage that makes the Hacker News crowd love them so much. They're not any better at predicting byte strings that can be compiled or interpreted into executable code than humans are if you gave both infinite time to do it, but they're a lot faster.
Yes, and by the same token, multicellular organisms are literally just sophisticated mitosis and survival optimizers for our cells. But when you take that optimization "to the limit" the cells develop weird things like body plans and back pain and Mozart.
Both examples involve the same "aha" moment: even though it's true that you are literally 'just' doing XYZ, unbelievably complex patterns and sub-goals can emerge.
> and by the same token
I don't think you intended this, but the word choice here gave me a chortle.
Vacuous, like calling a V8 a “next piston firing predictor” because engines are designed so that one piston sets up the next in the firing order and technically there’s some nonzero probability any piston can (mis)fire next. It’s missing two pieces:
1. Useful work that has been done (the previously generated token sequence :: the mechanical work already accomplished)
2. The role of structure in relation to the application (post-training :: other components like crankshaft etc)
A V8 does not "predict" the firing of the next piston, it triggers the firing of the next piston at a precisely controlled time with a spark plug (or a fuel injection nozzle in the case of a diesel engine).
The output of the LLM is literally a probability distribution of what the most likely next token is.
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100%
But it is a next token predictor.
Recursively invoked.
With carefully selected context.
And massive investment in RL to tune token selection.
And the ability to use cli tools on other folks' machines.
That's a powerful system built around a conceptually simple technology: Next token predictors.
Yes this is correct. The thing is not about the term next-token predictor being correct, but because of the connotative weight of that phrase as a implicit trivialization of LLM abilities, which is how it is often used.
What is the motivation behind advocating against people trivializing LLMs? As in, why do you care?
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From another point of view, campaigning against the "next token predictor model" is a means to implicitly inflate LLMs' abilities. Given all the other hype-inducing terminology we've seen--"reasoning", most egregiously IMO--this seems more likely. Is there a simple, more accurate mental model? From what I've seen of the literature, "next token predictor" is a very accurate first order description of what an LLM does, I can't really do better, therefore this or that connotative interpretation isn't giving me a great deal of pause.
Good example.
It’s also like saying our brains are just electric circuitry incorporated in meat. It’s true but it seems that consciousness emerges from this.
The fact that LLMs are next token predictors isn’t the interesting or impressive part. Actually my brain strictly is a black box predicting (or choosing) my next word/action/move… based on a complex existing context (my thoughts, the environment, my physical state, my senses…).
FWIW, I don’t believe LLMs are sentient, but I don’t think either that we have enough knowledge to rule it out.
That is the point: our minds are also next-„token“-predictors, at least we can‘t prove they‘re not. That‘s why I don‘t agree with the article: LLMs _are_ next-token predictors. However, that says little about their capabilities. Also, while I have no idea what „consciousness“ is, I have difficulties believing that it could arise in a program that, in theory, you could execute with pen and paper.
> our minds are also next-„token“-predictors, at least we can‘t prove they‘re not
Your mind can pick a random number without outputting it, participate in a short conversation, and then say the number.
We also can't prove that our minds aren't machine elf meat puppets. Come on. Please.
> It’s true
It's not. "Brains as electrical circuits" is a gross simplification based on our ignorance and prejudices. (In the 18th century they spoke of brains as "clockwork mechanisms".)
LLMs, in contrast, are literally next token predictors. We know exactly how LLMs work, and they are exactly that.
> We know exactly how LLMs work
I think you imply a rather loose standard for "exactly" here. I wouldn't even say this of major deterministic software projects that are orders of magnitude smaller than frontier LLM weight-dumps. In principle we could work our way through these systems eventually, sure, maybe even a single person could do so. But if we really understand exactly how our software works, how have we been tolerating bugs that lay dormant for years before being discovered by AI-assisted processes?
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imply that there is some fundamental limit on their capacities
This is a wildly dismissive statement that does a lot of heavy lifting. Your assertion is that we just happened to hit on a methodology that has no limitations between being an encyclopedia with a novel human language interface and, I guess by implication, AGI?
That seems more outrageous a claim than the one you're dismissing.
I don't think it's outrageous when many of the people who claimed it was a next-token predictor have been proven wrong repeatedly over the past 5 years. There were people years ago who claims AI could never answer questions like "what would happen to a ball on a table if I moved the table" correctly because its text-base world model could never intuit physics, or that it could never do math or code accurately.
When I say there is some issue with people claiming there is some fundamental limit on the capacities of LLM's, I don't mean to say "If you think that they don't have unlimited potential you are wrong", I mean "you can't use the architecture of the transformer to make a sweeping declaration of things LLM's can or cannot do without empirical evidence, because the empirical evidence has unearthed far more surprising revelations than a reductive theory has been able to"
I'm actually extremely confident that I can use the architecture to make a sweeping claim on what it can or can't do and will be extremely surprised if proven wrong:
A pure next-token language model won't be able to give detailed instructions to an ensemble of motors, mimicking a human body, to do a wide variety of tasks our human brain is excellent at doing, for example, inserting keys into a car, opening the door, sitting down, starting the car, putting the car in reverse, and exit a parking lot, being careful not to hit anything.
> it could never do math or code accurately.
They still can't do code accurately. The fact that you use this as a defense of your position greatly undermines the credibility of your claim.
Well I think in the absence of convincing pieces of evidence to the contrary you might be right. You’re making an empirical statement but we have already answered it today:
- we get novel, emergent properties and capabilities of these models that were not trained
- they have very clear generalization to out of domain problems
The point is people conflate the end product: a model that can clearly do very novel, useful and interesting things, with the vehicle for getting there which is a series of optimization steps involving next token prediction loss.
You mention limitations; we all clearly know the practical limitations of these models today, but if you look at scaling laws and empirical performance trends (epoch capability index for example) as well as the trajectory over the last couple of years (very stable), the claim that there is some sort of fundamental limitation is now surprisingly the claim that has the burden of proof.
You can claim it may be e.g. finite context. That is fundamentally bad for certain classes of tasks. This was the hypothesis of a lot of lab leadership of urgently trying to anticipate how to get around this bottleneck (still of course lots of work on this) but the surprising thing is it does not appear to be at this point a blocker.
The stacked transformer paradigm picks out points in circuit design space. It is very possible this architecture has no inherent limitations on what it can compute in principle.
Next token prediction is just an interface. It can be backed by a Markov chain, a neural model or an actual human being.
And what's wrong with downplaying the abilities and faculties of AI models if that's what people feel like saying? We don't call humans or animals sacks of chemicals because we believe they have moral status.
> used most of the time with a motive to downplay the abilities and faculties of AI models
Exactly. We're dancing around the real argument: there's massive amounts of influencing going on (and not only about AI.)
That's literally what LLMs are.
No amount of cope and anthropomorphizing is gonna change that cold, hard fact.
P.S. The perceived magic of LLMs comes from the way they cross-correlate all the probabilities of tokens on their context window. Not from their ability to "think ahead". They can't do that by design.