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Position: LLMs Can't Jump

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Re: Position: LLMs Can't Jump

#51

Post by jebarker »

ben_w wrote: post #38
jvanderbot wrote: post #20

Earlier quoted context omitted.

Even simpler: Can GPT-2 anticipate and build Gwen/Deepseek? I think the answer is almost trivially "no", so I wonder what changed?

Lots of things changed, GPT-2 is small (1.5e9) and is also a base model, so it is only doing next-token/autocomplete rather than prompt-response like even the first ChatGPT-3.5 was doing.

Just for the sake of clarity: all LLMs up to today are still only doing next-token/autocomplete. The training process got additional stages to shape the model weights, but standalone LLMs are still deployed essentially identically.

Re: Position: LLMs Can't Jump

#52

Post by inigyou »

elar_verole wrote: post #17
jvanderbot wrote: post #2

Came for: "A computer once beat me at chess, but it was no match for me at kick boxing." TFA was actually about leaps of intuition, sadly. One of the experiments I've heard proposed around here is to somehow create an LLM from all text up to 1980 or 1990 and see if it can get back to making itself.

I think this can't work because an LLM needs too much data, and before the internet there probably just wasn't enough to get close to what we have now

Why couldn't an LLM, if it was smart enough, generate and consume its own data?

I know the answer: because it leads to model collapse. But why is that? Wouldn't a smart model not collapse? It's seeming like they keep getting smarter because we keep pouring more of our own knowledge into them, not because they are actually getting smarter. And yes, sometimes a dumb but persistent bruteforcer can make new discoveries.

Re: Position: LLMs Can't Jump

#55

Post by emp17344 »

wildfireday2 wrote: post #46
defgeneric wrote: post #24

Worth reposting a follow-up tweet from the author Tom Zahavy [1] after this made the rounds on X/Twitter recently: > A few reflections on my "LLMs Can’t Jump" paper: > My position paper recently got some traction here, so I wanted to share a few thoughts and clarify a few things. > First things first: some people are framing this as "DeepMind is throwing cold water on AI for science" or claiming the paper argues LLMs…

Moreover anyone glomming onto this paper for goal-post-shifting “AI can never” should: 1. Read the last sentence of the abstract, and 2. Reflect that frontier reasoning agents already increasingly integrate multimodal models.

The worst thing about the AI boom is how tech bros feel comfortable abusing the goalpost fallacy. So annoying.

Re: Position: LLMs Can't Jump

#56

Post by Garlef »

inigyou wrote: post #52
elar_verole wrote: post #17

Earlier quoted context omitted.

I think this can't work because an LLM needs too much data, and before the internet there probably just wasn't enough to get close to what we have now

Why couldn't an LLM, if it was smart enough, generate and consume its own data? I know the answer: because it leads to model collapse. But why is that? Wouldn't a smart model not collapse? It's seeming like they keep getting smarter because we keep pouring more of our own knowledge into them, not because they are actually getting smarter. And yes, sometimes a dumb but persistent bruteforcer can make new discoveries.

> if it was smart enough

and i think this is exactly the crux;

the really big models need really big datasets

and current gen LLMs get a lot of training data beyond "all books + all of the internet"

the objection is then that producing this additional data would already confound it with pre "virtual cutoff date" knowledge (since the training data probably implies mathematical and SWE concepts that were developed post "virtual cutoff date")

Re: Position: LLMs Can't Jump

#57

Post by smusamashah »

inigyou wrote: post #52
elar_verole wrote: post #17

Earlier quoted context omitted.

I think this can't work because an LLM needs too much data, and before the internet there probably just wasn't enough to get close to what we have now

Why couldn't an LLM, if it was smart enough, generate and consume its own data? I know the answer: because it leads to model collapse. But why is that? Wouldn't a smart model not collapse? It's seeming like they keep getting smarter because we keep pouring more of our own knowledge into them, not because they are actually getting smarter. And yes, sometimes a dumb but persistent bruteforcer can make new discoveries.

If it is smart enough to generate data it can consume to train itself better, it is already smart enough to not need to do that.

Re: Position: LLMs Can't Jump

#59

Post by gus_massa »

defgeneric wrote: post #24

Worth reposting a follow-up tweet from the author Tom Zahavy [1] after this made the rounds on X/Twitter recently: > A few reflections on my "LLMs Can’t Jump" paper: > My position paper recently got some traction here, so I wanted to share a few thoughts and clarify a few things. > First things first: some people are framing this as "DeepMind is throwing cold water on AI for science" or claiming the paper argues LLMs…

>> Specifically, I focused on the equivalence principle—a key axiom that Einstein formulated through thought experiments grounded in his physical intuition.

It's weird because the equivalence principle is very unintuitive. Aristotle's Mechanics does not have it. It took almost two thousand years to discover inertia that is the most simple version of the equivalence principle. Einstein understood the idea of the the equivalence principle because he had a physics degree, not because he feel that in real life.

Moreover, if you ever have to study or teach Quantum Mechanics, physical intuition gets in the way. A lot of properties contradict the physical intuition but after a while you get use to them. If we continue with Einstein, the photoelectric effect does not aperar in real life.

Re: Position: LLMs Can't Jump

#60

Post by skew-aberration »

pama wrote: post #34

The early physics background is messy and incorrect. I didnt read the full position paper, but from its start: The Lorentz transformations were by Lorentz, well before Einstein’s paper on special relativity; the principle of relativity also existed before the Einstein paper. The math was all there, with steps taken by Maxwell, Voigt, Larmor, Lorentz, and Poincare. Einstein supplied a clean physical interpretation, ma…

Yes, I made a similar comment on the other mention today. The author seems to misunderstand what GR is / what it added to physics too (creating self-referential field equations to handle mass/energy equivalence - linear field equations without instantaneous 'action-at-a-distance' existed much earlier). The notion that it was a 'small signal' is totally false. Once you've hypothesized that the apparent mass of objects depends on the observer, you need to show that your theory gives consistent results for trajectories of objects in gravitational fields.

https://news.ycombinator.com/item?id=49177965

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