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I don't know how you get here from “predict the next word”

grumpy-economist.com

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Re: I don't know how you get here from “predict the next word”

#51

Post by ejolto »

callmeal wrote: post #29

The "predict the next word" to a current llm is at the same level as a "transistor" (or gate) is to a modern cpu. I don't understand llms enough to expand on that comparison, but I can see how having layers above that feed the layers below to "predict the next word" and use the output to modify the input leading to what we see today. It is turtles all the way down.

There is a big difference, because I understand how those transistors produce a picture on a screen, I don’t understand how LLMs do what they do. The difference is so big that the comparison is useless.

Re: I don't know how you get here from “predict the next word”

#52

Post by hnfong »

fc417fc802 wrote: post #22
belZaah wrote: post #12

It’s called emergent behavior. We understand how an llm works, but do not have even a theory about how the behavior emerges from among the math. We understand ants pretty well, but how exactly does anthill behavior come from ant behavior? It’s a tricky problem in system engineering where predicting emergent behavior (such as emergencies) would be lovely.

> but do not have even a theory about how the behavior emerges from among the math Actually we have an awful lot of those. I'm not sure if emergent is quite the right term here. We carefully craft a scenario to produce a usable gradient for a black box optimizer. We fully expect nontrivial predictions of future state to result in increasingly rich world models out of necessity. It gets back to the age old observation…

> black box optimizer

This is admission we don't know how it emerges.

Sure, we expect the behavior to emerge, but we don't know how.

Re: I don't know how you get here from “predict the next word”

#53

Post by imiric »

libraryofbabel wrote: post #17

I have come to think “predict the next token” is not a useful way to explain how LLMs work to people unfamiliar with LLM training and internals. It’s technically correct, but at this point saying that and not talking about things like RLVR training and mechanistic interpretability is about as useful as framing talking with a person as “engaging with a human brain generating tokens” and ignoring psychology. At least A…

Technical concepts can be broken down into ideas anyone can understand if they're interested. Token prediction is at the core of what these tools do, and is a good starting point for more complex topics.

On the other hand, calling these tools "intelligent", capable of "reasoning" and "thought", is not only more confusing and can never be simplified, but dishonest and borderline gaslighting.

Re: I don't know how you get here from “predict the next word”

#54

Post by anon7725 »

selridge wrote: post #34
wavemode wrote: post #24

> the kind of analysis the program is able to do is past the point where technology looks like magic. I don’t know how you get here from “predict the next word.” You're implicitly assuming that what you asked the LLM to do is unrepresented in the training data. That assumption is usually faulty - very few of the ideas and concepts we come up with in our everyday lives are truly new. All that being said, the refine.in…

>You're implicitly assuming that what you asked the LLM to do is unrepresented in the training data. This is just as stuck in a moment in time as "they only do next word prediction" What does this even mean anymore? Are we supposed to believe that a review of this paper that wasn't written when that model (It's putatively not an "LLM", but IDK enough about it to be pushy there) was trained? Does that even make sense?…

“Represented in the training data” does not mean “represented as a whole in the training data”. If A and B are separately in the training data, the model can provide a result when A and B occur in the input because the model has made a connection between A and B in the latent space.

Re: I don't know how you get here from “predict the next word”

#55

Post by measurablefunc »

brookst wrote: post #44
measurablefunc wrote: post #8

Earlier quoted context omitted.

What's the latest novel insight you have encountered?

Not the person you asked, and “novel” is a minefield. What’s the last novel anything , in the sense you can’t trace a precursor or reference? But.. I recently had a LLM suggest an approach to negative mold-making that was novel to me. Long story, but basically isolating the gross geometry and using NURBS booleans for that, plus mesh addition/subtraction for details. I’m sure there’s prior art out there, but that’s tr…

I don't know, that's why I asked b/c I always see a lot of empty platitudes when it comes to LLM praise so I'm curious to see if people can actually back up their claims.

I haven't done any 3D modeling so I'll take your word for it but I can tell you that I am working on a very simple interpreter & bytecode compiler for a subset of Erlang & I have yet to see anything novel or even useful from any of the coding assistants. One might naively think that there is enough literature on interpreters & compilers for coding agents to pretty much accomplish the task in one go but that's not what happens in practice.

Re: I don't know how you get here from “predict the next word”

#56

Post by selridge »

wavemode wrote: post #41
selridge wrote: post #34

Earlier quoted context omitted.

>You're implicitly assuming that what you asked the LLM to do is unrepresented in the training data. This is just as stuck in a moment in time as "they only do next word prediction" What does this even mean anymore? Are we supposed to believe that a review of this paper that wasn't written when that model (It's putatively not an "LLM", but IDK enough about it to be pushy there) was trained? Does that even make sense?…

Statistical models generalize. If you train a model that f(x) = 5 and f(x+1) = 6, the number 7 doesn't have to exist in the training data for the model to give you a correct answer for f(x+2) Similarly, if there are millions of academic papers and thousands of peer reviews in the training data, a review of this exact paper doesn't need to be in there for the LLM to write something convincing. (I say "convincing" rath…

Ok cool cool. Instead of pretending you need to teach me, you could engage with what I'm saying or even the OP!

"I don't know how you get here from "predict the next word"" is not really so much a statement of ignorance where someone needs you to step in but a reflection that perhaps the tech is not so easily explained as that. No magic needs to be present for that to be the case.

Re: I don't know how you get here from “predict the next word”

#57

Post by intended »

The article talks about LLMs reviewing Econ papers.

I’m hesitant to call this an outright win, though.

Perhaps the review service the author is using is really good.

Almost certainly the taste, expertise and experience of the author is doing unseen heavy lifting.

I found that using prompts to do submission reviews for conferences tended to make my output worse, not better.

Letting the LLM analyze submissions resulted in me disconnecting from the content. To the point I would forget submissions after I closed the tab.

I ended up going back to doing things manually, using them as a sanity check.

On the flip side, weaker submissions using generative tools became a nightmare, because you had to wade through paragraphs of fluff to realize there was no substantive point.

It’s to the point that I dread reviewing.

I am going to guess that this is relatively useful for experts, who will submit stronger submissions, than novices and journeymen, who will still make foundational errors.

Re: I don't know how you get here from “predict the next word”

#58

Post by heavyset_go »

visarga wrote: post #33

> Nothing you write will matter if it is not quickly adopted to the training dataset. That is my take too, I was surprised to see how many people object to their works being trained on. It's how you can leave your mark, opening access for AI, and in the last 25 years opening to people (no restrictions on access, being indexed in Google).

Most people value their time and work and don't want to give it away for free to some billionaire so they can reproduce it as slop for their own private profit.

That's to say, most people recognize when they're getting fucked over and are correct to object to it.

Re: I don't know how you get here from “predict the next word”

#59

Post by vasco »

qsera wrote: post #28
libraryofbabel wrote: post #17

I have come to think “predict the next token” is not a useful way to explain how LLMs work to people unfamiliar with LLM training and internals. It’s technically correct, but at this point saying that and not talking about things like RLVR training and mechanistic interpretability is about as useful as framing talking with a person as “engaging with a human brain generating tokens” and ignoring psychology. At least A…

>“predict the next token” is not a useful way That is the exact thing to say because that is exactly what it does, despite how it does so. It is not useful to say it if you are an AI-shill though. You bought up AI-hater, so I think I am entitled to bring up AI-shills.

My neurons are also just passing electric signals back and forward and exchanging water and salts with the rest of my body.
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