Comment by mort96
2 days ago
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.
2 days ago
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.
> 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.
There's nothing more valuable about them. Their advantage is they alone are affordable.
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.
I don't that's a fair description of either 'next-token predictor' or 'stochastic parrot'. Both of those terms describe mechanism, not value--the fact that people squawk that the terms are minimising is projection on their part, not inherent to the phrase.
3 replies →
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.
Agentic behaviors don't require end-users to be aware of tokens at all. Also, we literally say human actors have great chemistry :)
1 reply →
It can be pretty helpful to think of human function in chemical terms. Its at least unhelpful to deny it.
Milo Yiannopoulos used to think of other human beings as bags of chemicals until they deported his sack of shit molecules to the UK.
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.
> A V8 does not "predict" the firing of the next piston
It kind of does, though. In a gasoline engine you need to spark the combustion in advance of the piston reaching top dead-center to ignite the fuel early enough that it is able to provide downward pressure on the piston as it rolls over top dead-center. The amount of advance required changes with RPM, fuel octane, etc.
Start of delivery timing in a diesel is similar. You have to do it sufficiently far in advance to account for compressibility of the injection lines, fuel burn rate, etc as a function of RPM. A mechanical governor on an injection pump has a timing advance device built in. Electronically governed injection pumps, or modern common rail systems, do that in software.
So mechanically, engines kind of "predict" the next combustion event. Even moreso when you consider a modern ECU, which may be working at nanosecond resolution to time multiple injection events per cycle. To do this at such a resolution it will have to send signals to components based on a predictive model derived from "past" sensor data. E.g. it needs to act ahead of time to account for electrical and mechanical delays in the system.
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