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Why do LLMs have emergent properties?

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Re: Why do LLMs have emergent properties?

#101

Post by K0balt »

scopemouthwash wrote: post #31
K0balt wrote: post #6

Earlier quoted context omitted.

A decent thought-proxy for this : powered flight. An aircraft can approach powered flight without achieving it. With a given amount of thrust or aerodynamic characteristics, the aircraft will weigh dynamic_weight=(static_weight - x) where x is a combination of the aerodynamic characteristics and the amount of thrust applied. In no case where dynamic_weight>0 will the aircraft fly, even though it exhibits characterist…

“Thought-proxy”? I think the word you’re looking for is “analogy”.

Analogy is a great word proxy for thought proxy.

Yeah, I’m pretty sure analogy would have been fine there, I think maybe it fell off the edge of my vocabulary for a moment? Not really sure, but I really can’t think of any reason why “thought proxy” would have been more descriptive, informative, or accurate ¯_(ツ)_/¯

Re: Why do LLMs have emergent properties?

#102

Post by Retric »

chii wrote: post #96
Retric wrote: post #79

Earlier quoted context omitted.

It’s not about being “a runaway hit” as an objective measurement it’s about the things an LLM would need to achieve before that was possible. At first AI scores on existing tests seemed like a useful metric. However, tests designed for humans make specific assumptions that don’t apply to these systems making such tests useless. AI is very good at gaming metrics so it’s difficult to list some criteria where achieving…

> however a hit requires more than a reskinned story. demonstrably false with a lot of hits in the past that is a reskinned story of existing stories!

While not a technical term of art, copyright applies to a reskinned story. “the series is not available in English translation, because of the first book having been judged a breach of copyright.” https://en.wikipedia.org/wiki/Tanya_Grotter

There’s plenty of room to take inspiration and go in another direction aka Pride and Prejudice and Zombies.

Re: Why do LLMs have emergent properties?

#103

Post by unsupp0rted »

Jensson wrote: post #97
andy99 wrote: post #80

Earlier quoted context omitted.

A better bird analogy would be if we didn't understand at all how flight worked, and then started throwing rocks and had pseudo-intellectuals saying "how do we know that isn't all that flight is, we've clearly invented artificial flight".

> "we've clearly invented artificial flight" Scaling laws shows that the harder we throw the rock the further we fly, we just have to throw them hard enough and we have invented flying rocks! And for the naysayers out there, lemme throw this rock at your head and then tell me it isn't real!

If your goal is to get to stable orbit, even never having learned to fly, then the brute force approach works too

Re: Why do LLMs have emergent properties?

#104

Post by gond »

eutropia wrote: post #100

I often find that people using the word emergent to describe properties of a system tend to ascribe quasi magical properties to the system. Things tend to get vague and hand wavy when that term comes up. Just call them properties with unknown provenance.

> Just call them properties with unknown provenance.

They would if it would be the correct designation, however, it is not.

Emergence does not equal non-understanding or some spooky-hooky force coming from the unknown.Reductionism does not lead to an explaining-away of emergence.

Re: Why do LLMs have emergent properties?

#105

Post by Der_Einzige »

vonneumannstan wrote: post #20
andy99 wrote: post #15

I didn't follow entirely on a fast read, but this confused me especially: The parameter count of an LLM defines a certain bit budget. This bit budget must be spread across many, many tasks I'm pretty sure that LLMs, like all big neural networks, are massively under-specified, as in there are way more parameters than data to fit (understanding the training data set is bigger than the size of the model, but the point i…

This doesn't seem right and most people recognize that 'neurons' encode for multiple activations. https://transformer-circuits.pub/2022/toy_model/index.html

They’re 1000% right on the idea that most models are hilariously undertrained

Re: Why do LLMs have emergent properties?

#106

Post by atoav »

kevinsync wrote: post #82
anon373839 wrote: post #66

I remain skeptical of emergent properties in LLMs in the way that people have used that term. There was a belief 3-4 years ago that if you just make the models big enough, they magically acquire intelligence. But since then, we’ve seen that the models are actually still pretty limited by the training data: like other ML models, they interpolate well between the data they’ve been trained on, but they don’t generalize…

My hot take is that what some people are labeling as "emergent" is actually just "incidental encoding" or "implicit signal" -- latent properties that get embedded just by nature of what's being looked at. For instance, if you have a massive tome of English text, a rather high percentage of it will be grammatically-correct (or close), syntactic and understandable, because humans who speak good English took the time to…

Emergent as I have known and used it before is when more complex behavior emerges from simple rules.

My goto example for this was Game of Life, where from very simple rules, a very organically behaving (turing complete) system emerges. Now Game of Life is a deterministic system, meaning that the same rules and the same start-configurarion will play out in exactly the same way each time — but given the simplicity of the logic and the rules the resulting complexity is what I'd call emergent.

So maybe this is more about the definition of what we'd call emergent and what not.

As someone who has programmed markov chains where the stochastic interpolation really shines through, transformer-based LLMS definitly show some emergent behavior one wouldn't have immediately suspected just from the rules. Emergent does not mean "conscious" or "self-reflective" or anything like that. But the things a LLM can infer from its training data is already quite impressive.

Re: Why do LLMs have emergent properties?

#107

Post by adampwells »

zmmmmm wrote: post #81

This seems superficial and doesn't really get to the heart of the question. To me it's not so much about bits and parameters but a more interesting fundamental question of whether pure language itself is enough to encompass and encode higher level thinking. Empirically we observe that an LLM trained purely to predict a next token can do things like solve complex logic puzzles that it has never seen before. Skeptics c…

> However the novelty of what can be solved is very surprising.

I've read that the 'surprise' factor is much reduced when you actually see just how much data these things are trained on - far more than a human mind can possibly hold and (almost) endlessly varied. I.e. there is 'probably' something in the training set close to what 'surprised' you.

Re: Why do LLMs have emergent properties?

#108

Post by gond »

gond wrote: post #70
anon373839 wrote: post #66

I remain skeptical of emergent properties in LLMs in the way that people have used that term. There was a belief 3-4 years ago that if you just make the models big enough, they magically acquire intelligence. But since then, we’ve seen that the models are actually still pretty limited by the training data: like other ML models, they interpolate well between the data they’ve been trained on, but they don’t generalize…

Interesting. Is there a quantitative threshold to emergence anyone could point at with these smaller models? Tracing the thoughts of a large language model is probably the only way to be sure, or is it?

Disregarding the downvotes, I mean this as a serious question.

From the liked article: “We don’t know an “algorithm” for this, and we can’t even begin to guess the required parameter budget or the training data needed.”

Why not, at least the external ones? The computational resources and the size of the training dataset is quantifiable from an input point of view. What gets used is not, but the input size should.

Re: Why do LLMs have emergent properties?

#109

Post by lgas »

skydhash wrote: post #10
OtherShrezzing wrote: post #9

It feels like this can be tracked with addition. Humans expect “can do addition” is a binary skill, because humans either can or cannot add. LLMs approximate addition. For a long time they would produce hot garbage. Then after a lot of training, they could sum 2 digit numbers correctly. At this point we’d say “they can do addition”, and the property has emerged. They have passed a binary skill threshold.

Or you could cobble up a small electronic circuit or a mechanical apparatus and have something that can add numbers.

Sure, but then what you're doing would be irrelevant to this discussion.

Re: Why do LLMs have emergent properties?

#110

Post by bunderbunder »

dmd wrote: post #33
andy99 wrote: post #29

Earlier quoted context omitted.

Emergence in the context of LLMs is really just us learning that "hey, you don't actually need intelligence to do , turns out it can be done using a good enough next token predictor. We're basically learning what intelligence isn't as we see some of the things these models can do. I always understood this to be the initial framing, e.g. in the Language Models are Few Shot Learners paper but then it got flipped around…

Or maybe you need intelligence to be a good enough next token predictor. Maybe the thing that “just” predicts the next token can be called “intelligence”.

Maybe?

Mostly I just think that "Intelligence" and "AI" go together like "life, the universe and everything" and "42".

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