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

gmcgoldr.github.io

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

#221

Post by danielmarkbruce »

Dylan16807 wrote: post #196
danielmarkbruce wrote: post #167

Earlier quoted context omitted.

Yup, you are mostly right. I guess the people in my camp find the "it's just a next token predictor" stupid in that it's like saying "it's just a bunch of carbon and hydrogen", but it's also one of those things where people like to think they are clever because they think they are theoretically correct. But they aren't even that. So it's like double stupid. But the "next token predictor" part is at least technically…

It really is an important distinction, though. Being a next token predictor doesn't stop it from writing good sentences, but it does mean an LLM by itself can't play the number guessing game with you.

This is pedantic, but, actually RL has improved the quality of sentence construction in LLMs quite dramatically... And once you do some RL on that model, it aint a next token prediction machine any longer.

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

#222

Post by saghm »

jameshart wrote: post #145
saghm wrote: post #129

Earlier quoted context omitted.

I don't find the fact that I don't call any humans "action predictors" to be a particularly meaningful insight because my rationale is that it's a weird thing to call a human; football coaches can do plenty of other things besides just coaching football.

Yep, we’re all just putting one foot in front of the other, hoping we’re doing the right thing to bring about the outcomes we want, trying our best. But that’s the point: so is an LLM. Putting one token in front of another, hoping it’s doing the right thing to bring about the rewards it’s trained to… trying its best. So yeah, not ‘next token predictors’. ‘Next token tryers’ maybe.

There's a pretty huge difference in our understanding of the methodology of how LLMs make decisions and how humans make decisions, so I don't understand why you're arguing that anything about how humans make decisions is relevant to the terminology we use for LLMs.

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

#224

Post by hellohello2 »

drivebyhooting wrote: post #183

That chess analogy deeply confused me. Chess engines don’t compute win probabilities and choose the highest move. I don’t think a chess engine is an apt analogy at all. In a chess engine, there is a concrete search tree and although it emits one move at a time, it’s actually picking the entire branch (of course, with iterative deepening as the game progresses). There is no obvious place in transformer models where th…

Imagine a checkers engine then.

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

#225

Post by [deleted] »

danielmarkbruce wrote: post #213
dijksterhuis wrote: post #205

Earlier quoted context omitted.

> Nobody says a system or product works a certain way and means the system while it's half built. "Bridges drop cars in the water!". Right. To understand how an engine works, it's important to understand what a piston does as part of the engine.

You are conflating "half built" with "a piece of a system". The model weights change as the model goes through the training process. They aren't stored after pre-training is done and other weights are put somewhere else. It's more like pottery - the thing changes. It's not correct to say something is soft and malleable because it once was.

[deleted]

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

#228

Post by roenxi »

scragz wrote: post #207
roenxi wrote: post #179

Earlier quoted context omitted.

> It's just not predicting based on it's training data, but predicting based on RLVR & more, trying to get to the optimal solution ( as much as the solutions CAN be optimal) It is predicting based on a model. In many cases we can download the model off hugging face. The model is conditioned by all sorts of things. Training data, post-training, coincidence, prompt inputs, runtime data available from whatever means. >…

there are some diffusion text models.

That is logically equivalent to a serial token prediction engine. If you have a diffusion text model you can use it to implement a serial token generator and if you have a serial token generator you can use that to implement a diffusion-generated text string. Don't think about the efficiencies of that, it is an upsetting idea (eg to generate N tokens, the serialising model might recalculate the same string N times from an input and emit one token of it each time - rather wasteful).

It is similar to how everything ends up being Turing complete. Any prediction system has to be equivalent to some sufficiently complicated text generation system to describe the prediction. And any text-generation system has to be equivalent to a sufficiently complicated model that serially emits tokens.

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

#229

Post by dijksterhuis »

danielmarkbruce wrote: post #213
dijksterhuis wrote: post #205

Earlier quoted context omitted.

> Nobody says a system or product works a certain way and means the system while it's half built. "Bridges drop cars in the water!". Right. To understand how an engine works, it's important to understand what a piston does as part of the engine.

You are conflating "half built" with "a piece of a system". The model weights change as the model goes through the training process. They aren't stored after pre-training is done and other weights are put somewhere else. It's more like pottery - the thing changes. It's not correct to say something is soft and malleable because it once was.

> 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.

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i've got other stuff to do, so i'm stopping here.

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

#230

Post by danielmarkbruce »

dijksterhuis wrote: post #229
danielmarkbruce wrote: post #213

Earlier quoted context omitted.

You are conflating "half built" with "a piece of a system". The model weights change as the model goes through the training process. They aren't stored after pre-training is done and other weights are put somewhere else. It's more like pottery - the thing changes. It's not correct to say something is soft and malleable because it once was.

> 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.
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