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Language Models Represent Space and Time

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Re: Language Models Represent Space and Time

#101

Post by YeGoblynQueenne »

circuit10 wrote: post #27
YeGoblynQueenne wrote: post #6

Earlier quoted context omitted.

This is a preprint that was just uploaded to arxiv two days ago. Don't be hasty and assume that it settles any matter at all. Many such claims have been made before and many counter-claims also. There is still a lively debate on the subject and it will be some time before there is agreement. More generally, any scholarly article is a claim, and should never be read as automatically true. That's something to keep in m…

I thought it was completely obvious that if it can predict things about the world as well as it can it has some form of world model, even if inaccurate. I’m surprised that anyone would argue that a language model doesn’t model things

Yes, I see what you mean. It's frustrating but the word "model" is severely overused in Computer Science and AI and it can cause a lot of confusion.

Briefly, a "world model" is a theory possessed by an autonomous agent that describes the entities that exist in the world and how they interact with each other and with the agent, and that the agent can use to make decisions. This is the sense in which "model" is used when people talk about "model based" approaches to AI (such as planning, for instance, which is "the model based approach to autonomous behaviour").

A "language model" on the other hand is a statistical model of the text in a corpus. A statistical model is really a set of events, and their probabilities. The "events" in a statistical language model are usually word collocations, where a collocation is a word A found near a word B, for some measure of "near". If you've heard about "word embeddings", that's a model of word collocations in text corpora.

So the claim that LLMs "have a model" is about LLMs being autonomous agents with a world-model, while "Large Language Model" is about statistical modelling. There's no reason why a statistical language model should have a world model.

Re: Language Models Represent Space and Time

#102

Post by nyrikki »

kridsdale3 wrote: post #84
nyrikki wrote: post #36

Earlier quoted context omitted.

Feed forward networks are pairwise linear in respect to inputs, they are effectively DAGs. In theory you could represent an entire LLM as a single 2 dimensional graph of linear line segments. It wouldn't be useful for much as the parameters are clustered in dense patches.

So are my synapses, and by and large my cortex is 2-dimensional as well (folded and scrunched in to a super complex shape by the limitations of my roughly spherical cranium)

Everything from ion channel response up is non-linear and not a deterministic Turing machine.

Random cite

https://www.jneurosci.org/content/13/1/334?ijkey=9f8730dd3d0...

Re: Language Models Represent Space and Time

#103

Post by [deleted] »

nyrikki wrote: post #97
famouswaffles wrote: post #88

Earlier quoted context omitted.

Yeah that's not the case lol. ann's can model any function even non linear ones.

Pairwise linear in respect to inputs, for feed forward networks like those that use attention is the restriction related to linear or linearizable. Those words are important. But we know that ML cannot model all turning machines in the limit. As to more reasonable models that gets more complex. Rice's theorem in respect to functions doesn't fit with you claim: For any non-trivial property of partial functions, no gen…

[deleted]

Re: Language Models Represent Space and Time

#104

Post by baq »

fipar wrote: post #66
nopinsight wrote: post #50

Earlier quoted context omitted.

GPT-4 should do significantly better, but still worse than expert humans, on the tasks you mentioned. They would also benefit from good prompting, such as those in - https://lilianweng.github.io/posts/2023-03-15-prompt-enginee... - https://help.openai.com/en/articles/6654000-best-practices-f... Not a large percentage of humans would be able to do the tasks listed above without significant experience or training eithe…

Thanks, I'll wait for it to be available (I'm a casual user and I'm not the one setting this up or paying for it, if someone is paying for it, so I'm not in control of which version I use). I'm in full agreement about humans and reasoning, btw. I just don't think chatgpt (with the version I've used) is anywhere on the same league as the worst (way below-average) humans either. I do think it's quite useful as a writin…

Try Bing chat, it has a GPT-4 mode, which you have to look for but it’s there and free.

Re: Language Models Represent Space and Time

#105

Post by YeGoblynQueenne »

famouswaffles wrote: post #35
YeGoblynQueenne wrote: post #19

Earlier quoted context omitted.

For me, one of the most interesting things that have come out of LLMs is the confirmation that humans are very bad at reasoning and, consequently, its' a very bad idea to try and make machines that "think like humans", because that way we'll only make machines with none of the advantages of machines and all the disadvantages of computers. For instance -I'm not trying to be mean and I'm certainly not blaming you in pa…

>because that way we'll only make machines with none of the advantages of machines and all the disadvantages of computers. There is no evidence, basically none whatsoever that general "perfect logical reasoning" is a thing that actually exists in the real world. None. No animal we've observed does it. Humans certainly don't do it. The only realm this idea actually works is Fiction. and this was not like for a lack of…

That's a common take but it doesn't really hold any water: computers are logic machines and all of Computer Science is based on logic; and it works just fine.

Besides which, you may not hear about them in the news but pretty much all the classical, symbolic- and logic-based approaches of Good, Old-Fashioned AI are still going strong and are doing very well thank you in tasks in which statistical machine learning approaches underperform.

To give a few examples: automated planning and scheduling (used e.g. by NASA in its autonomous guidance systems for its spaceships and rovers), program verification and model checking (the latter has transformed the semiconductor industry and led to several recent Turing awards), SAT-solving and constraint satisfaction (where recent algorithmic advances have made it possible to solve many instances of NP-complete decision problems in polynomial time), adversarial search (AlphaGo and friends aren't going anywhere without Monte Carlo Tree Search), program synthesis (you can generate code with LLMs, but good luck if you want it to work correctly), automated theorem proving, heuristic search, rule learning, etc etc.

To clarify, those are all logic-based approaches that remain the state of the art in classical AI tasks where statistical machine learning has made no progress in the last many decades. You may not read about them in the news and they're not even considered "AI" by many, but that's because they work and work very well, and the "AI Effect" takes hold [1].

Even poor old expert systems are the de facto standard for expressing business logic in the software industry. I guess. Informally, of course.

_________________

[1] https://en.wikipedia.org/wiki/AI_effect

Re: Language Models Represent Space and Time

#106

Post by wnoise »

uoaei wrote: post #40
semitones wrote: post #18

Earlier quoted context omitted.

source?

It was something like Paris - France + Japan = Tokyo I know that's not a source but you could also just google "word2vec", as I recall most of the explainer blog posts had similar examples.

That gets "analogies" rather than "spatial awareness" per-se.

Re: Language Models Represent Space and Time

#107

Post by wnoise »

haltist wrote: post #87

> coherent model of the data generating process -- a world model. I'd never seen a definition of world model but this seems deficient in several ways because it does not mention anything about abstraction and logical reasoning.

Those aren't part of the model though. The model is just a representation of the world. An abstract representation can be useful, but so can more concrete ones. A representation that is amenable to logical reasoning is also useful, but the reasoning isn't part of the model.

Re: Language Models Represent Space and Time

#108

Post by [deleted] »

famouswaffles wrote: post #68
YeezyMode wrote: post #53

Earlier quoted context omitted.

Would you say that this in itself is due to how incomplete human reasoning is in the first place? That as a result, our ideas of logic and what perfect logic looks like are bound to fail? Or are you saying that the purest mathematical representation of logic cannot scale to a point where they can model and predict real world relationships successfully?

The second. Mathematical logic thrives on precision, clear definitions, and unambiguous axioms, but real-world systems are often marked by vagueness, uncertainty, and dynamic change. Gödel’s Incompleteness Theorems also demonstrates that in any sufficiently powerful mathematical system, there are true statements that cannot be proven within the system. This implies that no matter how refined a logical system you devi…

[deleted]

Re: Language Models Represent Space and Time

#109

Post by YeGoblynQueenne »

famouswaffles wrote: post #68
YeezyMode wrote: post #53

Earlier quoted context omitted.

Would you say that this in itself is due to how incomplete human reasoning is in the first place? That as a result, our ideas of logic and what perfect logic looks like are bound to fail? Or are you saying that the purest mathematical representation of logic cannot scale to a point where they can model and predict real world relationships successfully?

The second. Mathematical logic thrives on precision, clear definitions, and unambiguous axioms, but real-world systems are often marked by vagueness, uncertainty, and dynamic change. Gödel’s Incompleteness Theorems also demonstrates that in any sufficiently powerful mathematical system, there are true statements that cannot be proven within the system. This implies that no matter how refined a logical system you devi…

Gödel didn't say anything about real world phenomena. He was talking about formal languages and mathematics.

Re: Language Models Represent Space and Time

#110

Post by famouswaffles »

nyrikki wrote: post #97
famouswaffles wrote: post #88

Earlier quoted context omitted.

Yeah that's not the case lol. ann's can model any function even non linear ones.

Pairwise linear in respect to inputs, for feed forward networks like those that use attention is the restriction related to linear or linearizable. Those words are important. But we know that ML cannot model all turning machines in the limit. As to more reasonable models that gets more complex. Rice's theorem in respect to functions doesn't fit with you claim: For any non-trivial property of partial functions, no gen…

>But we know that ML cannot model all turning machines in the limit.

No computer is truly turing without infinite memory, including humans.

>Rice's theorem in respect to functions doesn't fit with you claim: For any non-trivial property of partial functions, no general and effective method can decide whether an algorithm computes a partial function with that property. The generalization problem being the important part.

This is...irrelevant. The theorem makes the claim for complete generalization (which humans do not demonstrate) as you say. It doesn't actually matter if the algorithm can't make the claim of property for all processes. Just like it doesn't matter that computers or humans are not really turing complete.

We don't need a computer that is exactly a human. We need a computer that works.

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