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How LLMs work

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Re: How LLMs work

#171

Post by someguyiguess »

gobdovan wrote: post #48
jfim wrote: post #17

Earlier quoted context omitted.

Indeed. It's pretty interesting to realize after implementing GPT-2 that the frontier models are scaled up versions of that, with various tweaks to improve performance, model-wise. The secret sauce though is all the datasets, RL training, knowledge of what works from doing all kinds of ablation experiments, and a massive compute moat.

The secret sauce is also having the necessary 'creativity' to not get ceased and desisted into oblivion and jail from all the copyrighted material you trained your model on. Btw, not making a moral judgement, [0] shows Michael and Dalton from YC discussing why Ilya Sutskever had to leave Google to pursue what's now ChatGPT [0] https://youtu.be/E8pvgN1j-Ck?t=748

And to make anyone who threatened to expose them “commit suicide”

Re: How LLMs work

#172

Post by robwwilliams »

malwrar wrote: post #10

Back when ChatGPT came out, I was so shocked by how _good_ it was for an “AI” product that I simply had to know how it worked. Over the next month I ended up drawing out a block diagram on a whiteboard I have in my office, with the math involved next to each step in the blackboard. I’d puzzle about each step along the way, and the triumph of completing the drawing was also that of this sense of deep understanding. I…

Great, and won’t we all be just as surprised when human self-attentional control turns out to be just as simple or just as complex! Our minds as a strange fabric built of threads of recursions without the benefit of any explicit clock.

Re: How LLMs work

#173

Post by otabdeveloper4 »

Ampersander wrote: post #104
AltruisticGapHN wrote: post #88

I don't like how most LLM explainer articles and videos say that essentially a LLM " predicts the next word". I'm a developer but not very good at maths and I still don't understand any of it. A LLM clearly has some "visual" capacity. You ask Gemini to build something with Canvas and it's able to reason about the shape of things. Like recently I waanted a checkbox that has like a gradient flowing around the edge. It…

I do agree bigly. Calling what is basically a superhuman brain inside a computer just a "token predictor" is peak thinkslop.

Inside the magic AI box is literally nothing but this loop:

    int n_tokens = 0;
    while (n_tokens
If you don't believe me then just download llama.cpp and see for yourself.

Re: How LLMs work

#174

Post by mjmsmith »

MagicMoonlight wrote: post #130
AltruisticGapHN wrote: post #88

I don't like how most LLM explainer articles and videos say that essentially a LLM " predicts the next word". I'm a developer but not very good at maths and I still don't understand any of it. A LLM clearly has some "visual" capacity. You ask Gemini to build something with Canvas and it's able to reason about the shape of things. Like recently I waanted a checkbox that has like a gradient flowing around the edge. It…

It can’t. It’s like a Redditor, it just repeats what it has seen other people say. It has read all of stackoverflow, so it has seen your kind of problem before. Try asking it something really unusual and it will shit the bed.

> It’s like a Redditor, it just repeats what it has seen other people say.

Can stochastic parrots understand irony?

Re: How LLMs work

#175

Post by otabdeveloper4 »

throw310822 wrote: post #97
AltruisticGapHN wrote: post #88

I don't like how most LLM explainer articles and videos say that essentially a LLM " predicts the next word". I'm a developer but not very good at maths and I still don't understand any of it. A LLM clearly has some "visual" capacity. You ask Gemini to build something with Canvas and it's able to reason about the shape of things. Like recently I waanted a checkbox that has like a gradient flowing around the edge. It…

LLMs are modelled to predict the next token, and are indeed trained to do so on enormous bodies of text. But to be really good at predicting the next token (word) at the end of a long string of text, you must understand what the text means . If I give you the entire text of a long novel and at the end ask you a single "yes/ no" question about the plot, you only need to emit a single token, but emitting the correct on…

> This is what LLMs do. They're generating meaningful, coherent text

No, they generate grammatically coherent text. That is because human language grammars are fundamentally mathematical structures that can be approximated with matrix operations.

They don't generate meaningful text because they have no inherent knowledge of the world.

If you've used LLMs for any amount of time you've already noticed how often they get confused about numeric quantities - like confusing notions of "bigger than" and "less than" or being unable to count letters in words.

This is because any meaning in their output is only accidental.

Re: How LLMs work

#176

Post by raincole »

AltruisticGapHN wrote: post #88

I don't like how most LLM explainer articles and videos say that essentially a LLM " predicts the next word". I'm a developer but not very good at maths and I still don't understand any of it. A LLM clearly has some "visual" capacity. You ask Gemini to build something with Canvas and it's able to reason about the shape of things. Like recently I waanted a checkbox that has like a gradient flowing around the edge. It…

> What I mean, is the LLM is able to represent things in space . That part I don't understand.

Why do you think this is mutually exclusive to "LLM predicts the next token"?

If you tell someone from 19th century that bytes (just 0s and 1s!) can represent an opera, a song, or even a whole interactive experience, they might be really confused. But there is no reason they can't.

If you tell someone without math background that the sums of smaller and smaller sin waves can represent pretty much anything in our universe, they might be really confused. But there is no reason they can't.

There is simply no reason that a next-token predicator can't generate a nice-looking checkbox.

Re: How LLMs work

#178

Post by perching_aix »

otabdeveloper4 wrote: post #43
jumploops wrote: post #22

Earlier quoted context omitted.

Those are all just optimizations. We still don’t really know why they work, we just know how to build them.

We do know how they work. They predict the next statistically most likely token. The "bitter lesson" is that fake-it-till-you-make-it is a valid way of doing knowledge work. (Or not make it, then people will just claim you're holding the LLM wrong and it's not the AI's fault.)

This "they just predict the next statistically most likely token" is such an handwavey and willfully misleading explanation, it's unreal, and I'm so fucking tired of seeing it so incessantly repeated. It's beyond asinine.

You know it perfectly damn well that a typical person's idea of statistics is not some insanely high cardinality stateful prediction, but a "well a coin toss is a 50:50, and a lottery win is a 1:100000000". You also know it perfectly damn well that as a result, people will just think that all the sentences chatbots ever produced to them were then just somewhere in the massive training set, letter by letter. This insinuation is often even explicitly appealed to.

And that picture is outright false. It's a statistical process, yes, so saying that it does what it does by "just doing statistics" is gonna be a generally correct description, but that's not at all inquisitive to how exactly does it do it, nor is it the zinger you think it is. If you did the aforementioned, you'd just get milquetoast nonsense, like you can see in the countless Markov-chain primers. And while the models do have a lot of the training set lossily captured, they do also absolutely generalize (that's how they can do that lossy compression), and you can quite literally find representations of those generalizations in them, and also see them activate.

It's like summarizing how any program works by just saying "well it just manipulates ones and zeroes". Not very informative, is it? Or how programs are written by just programmers sitting in a cushy office, ryhtmically pressing keys on a keyboard. Not a very fair or insightful description, which you'll know if you've done any amount of programming in your life on your own. Extends to all other white collar jobs too.

It's also not even true in the most literal sense: models can and do absolutely choose a less than maximally likely next token, that's what the various decoding parameters are for. "Maximally likely next token" further conviently skipping over how that likelihood is established in the first place, i.e. the literal point of the question, going in a cute little circle.

I'm so over this "stochastic parrot" bullshit.

Re: How LLMs work

#180

Post by CGMthrowaway »

zbendefy wrote: post #129
dist-epoch wrote: post #126

Earlier quoted context omitted.

Isn't that over-simplifying it a bit too much? You can go another step - a FFN can be simulated on a Turing machine, thus it just exemplifies the incredible semantical power of the Turing machine model of computation. (in fact you don't even need a Turing machine, since there is no looping in one forward pass). In theory you can run a huge FFN on the tiniest Turing machine, in practice it's much better to run a Trans…

Good point! There is also the case for Markov chains being theoretically able to do these if tuned well. Or even SAT problem.

"LLM is just fancy autocomplete"
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