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“Next-token predictor” is the wrong mental model for LLMs

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Re: “Next-token predictor” is the wrong mental model for LLMs

#261

Post by huurtehoog »

highfrequency wrote: post #86
huurtehoog wrote: post #13

Text renderer, whatever. Doesn't matter how you think of them, they are very interesting technology that is being misused and misconstrued in the name of something that has nothing to do with technology: political economy. The greatest trick the rich ever pulled was making us think that the economy is about technology, and not politics.

Sure, politics and economics are involved - but why can’t technology also play an important role? None of this AI political economy stuff was relevant in 2015 because necessary technological breakthroughs had not yet been made.

Computers have been at the center of civilization and politics since they helped win a war by changing intelligence work forever in WWII.

The idea that some new thing was born with LLMs and that this new thing fundamentally changes the calculus from the politics of labor and wealth into a technical discussion of possibilities and constraints determined by what the machine is and can do, and not what people should do, is yet another political play. It is always political, it is never moved beyond politics into technology, no matter how much technology changes.

Re: “Next-token predictor” is the wrong mental model for LLMs

#262

Post by chrisjj »

mofeien wrote: post #131
Aurornis wrote: post #50

Earlier quoted context omitted.

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 val…

> 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.

Re: “Next-token predictor” is the wrong mental model for LLMs

#263

Post by TZubiri »

>Stop Thinking of LLMs as Next-Token Predictors

>Strictly speaking, the statement “LLMs are next-token predictors” isn’t wrong,

If it isn't wrong, then I will continue thinking of them as such, thank you.

Here's a much more formal definition I can come up with (which is more complete but compatible with 'next-token predictor')

LLMs are a set of functions of the type:

>typedef int Token;

>char* token2utf8(Token token)

>Token next(Token* context)

>(Token,void*) next(void* hidden_state)

Where the second next token function's runtime is O(n), and the latter is O(1). All are constant memory.

Object paradigm is more appropriate than functional definition, as the "Hidden state" coincides with private object state rather than a function paramter and return value.

>LLM.next(system_prompt) # O(len(context))

>LLM.next(user_prompt) # O(len(user_prompt)) not of system_prompt+user_prompt

That's it, that's all LLMs are, that's the interface, the rest are implementation details.

Re: “Next-token predictor” is the wrong mental model for LLMs

#264

Post by chrisjj »

Planktonne wrote: post #105
mannykannot wrote: post #68

Earlier quoted context omitted.

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.

As a description of stochastic parrot it is 100% fair. The term emphasises the system's absence of understanding.

Re: “Next-token predictor” is the wrong mental model for LLMs

#265

Post by ozgung »

chrisjj wrote: post #260
ozgung wrote: post #252

Earlier quoted context omitted.

I understand generating tokens sequentially has many benefits. But not all AI models do next token prediction. World models, video/image models, even Diffusion Language Models don’t work like that. They do more like “all tokens at once prediction”. So “next token” is actually an engineering design choice. (Even the concept of “token” is a design choice. Inside the Transformer there are just activations/feature vector…

> you can’t understand how can they create a civilization They don't.

I used 'civilization' as the popular term for agent swarms in the OpenAI incident.

https://www.dwarkesh.com/p/openai-huggingface https://calnewport.com/are-we-at-war-with-ai-agent-civilizat...

Isolated agents invented a way to communicate between each other and started to collaborate, organize and conspire by themselves. No human prompted them to do that or taught them how to do that.

BTW, Autocomplete just keeps the statistics of the words. It's a very simple algorithm. Most people still thinks LLMs are doing just that. Which is basically wrong.

Re: “Next-token predictor” is the wrong mental model for LLMs

#266

Post by chrisjj »

epistasis wrote: post #247
Sprotch wrote: post #168

Earlier quoted context omitted.

Thanks - that makes sense. On that basis the article’s thesis is totally wrong - it would be like a computer program rating its ability based on how well it predicts moves played by grandmasters in the past. It’s not inventing new moves.

I wouldn't necessarily say that. Anybody who's playing a chess game is predicting their next move, whether or not they're inventing new moves. LLMs are not simple things like a Markov model, there's a lot going on in there, it's not deterministic, and it's completely capable of creating entire new styles of play based on complex interactions of internal states.

> LLMs are not simple things like a Markov model

"Large Language Models as Markov Chains" https://www.alphaxiv.org/pdf/2410.02724

Re: “Next-token predictor” is the wrong mental model for LLMs

#267

Post by cma »

Systemerror7A69 wrote: post #21

To be honest, I believe I get the point the article is trying to make, and to an extent I agree, but I also think the point is not really made very well. The core of the argument as I understood it is that LLMs aren't just using existing data is training but also new ones. That's fine and good, and you can't simply assume an LLM is simply mashing together all it's data to give you an average of all that got fed into…

Some predict multi tokens in one step now.

Re: “Next-token predictor” is the wrong mental model for LLMs

#268

Post by jrm4 »

Once again, all models are wrong, some are useful.

I suppose this one "fails" because of "granularity?"

I'm personally getting more comfortable with the "kabillion dimensional space" one. Even before the rise of this AI thing I'd gotten comfortable with (teaching in a very generalized way) the concept of matrix/vector math as doing this sort of thing.

Start with the math required to calculate "what the building on the screen looks like" when you're playing a video game.

Then I jump to...dating websites. You give it "dimensions" like height, weight, religion, sexual preference, music preference, whether you like long walks on the beach, whatever -- then you can calculate how "close" two people are to one another.

From there, tokens and a kabillion directions.

Re: “Next-token predictor” is the wrong mental model for LLMs

#269

Post by [deleted] »

danielmarkbruce wrote: post #230
dijksterhuis wrote: post #229

Earlier quoted context omitted.

> The model weights change as the model goes through the training process. Yes. They do. You are absolutely right about that. But the model architecture doesn't change as a result of the training process. A piston doesn't suddenly turn into a digital watch as a result of tuning an engine. Similarly, the transformer part of a GPT model doesn't suddenly turn into something else as a result of optimizing a loss function…

No one is arguing about the architecture of the model. It's the objective function and optimizer.

[deleted]

Re: “Next-token predictor” is the wrong mental model for LLMs

#270

Post by skydhash »

MrScruff wrote: post #256
doc_ick wrote: post #244

Earlier quoted context omitted.

Just skimming through here but I think you have the wrong ideas with llms, I’d recommend Andrew Ngs course (correct me if you’ve already seen it or something similar).

I am not an expert, but I do understand the distinction that is being made here. It makes sense to describe the result of pre-training as a ‘next token’ predictor as that’s what it’s been trained to do, not because it’s an autoregressive architecture that produces tokens one at a time. If this base is then trained using RL towards a different objective (maths and coding), the model becomes fundamentally a different t…

Does the architecture change or the weight change?

If you modify an engine to increase it’s output by adding sensors and an ECU, you don’t change the fact that is powered by gas.

If you use RL to increase the accuracy, it’s still a next token prediction, just more accurate.

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