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Why do LLMs have emergent properties?

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Re: Why do LLMs have emergent properties?

#91

Post by creer »

lordnacho wrote: post #21

What seems a bit miraculous to me is, how did the researchers who put us on this path come to suspect that you could just throw more data and more parameters at the problem? If the emergent behavior doesn't appear for moderate sized models, how do you convince management to let you build a huge model?

They didn't. Not LLM people specifically. Google a long time ago figured out that you get far better results on a very wide range of problems just by going bigger. (Which then must have become frustrating for some people because most of the effort seems to have gone to scaling? See for example as-opposed-to-symbolic.)

Re: Why do LLMs have emergent properties?

#92

Post by recursivecaveat »

waynecochran wrote: post #22

Since gradient descent converges on a local minima, would we expect different emergent properties with different initialization of the weights?

You end up with different weights when using different random initialization, but with modern techniques the behavior of the resulting model is not really distinct. Back in the image-recognition days it was like +/- 0.5% accuracy. If you imagine you're descending in a billion-parameter space, you will always have a negative gradient to follow in some dimension: local minima frequency goes down rapidly with (independent) dimension count.

Re: Why do LLMs have emergent properties?

#93

Post by creer »

teekert wrote: post #39

Perhaps we should ask: Why do humans pick arbitrary points on a continuum beyond which things are labeled “emergent”?

Sure but also see the benchmark creation to benchmark breaking race. The benchmark creation researchers have been doing their best to create difficult, lasting benchmarks that won't be broken by next week. They are not illiterate idiots. And yet the LLMs (or LLM-based systems really) have been consistently so far breaking the benchmarks in absurd time.

It's hard to say that nothing significant is going on.

Also the fine article's entire point is that these are not points on a continuum.

Re: Why do LLMs have emergent properties?

#94

Post by creer »

Michelangelo11 wrote: post #7

How could they not? Emergent properties are unavoidable for any complex system and probably exponentially scale with complexity or something (I'm sure there's an entire literature about this somewhere). One good instance are spandrels in evolutionary biology. The wikipedia article is a good explanation of the subject: https://en.m.wikipedia.org/wiki/Spandrel_(biology)

This is also my impression "how could they not" but it goes a bit further: Can we predict it? Can we estimate the size of a system that will achieve X? Can we build systems that are pre-disposed to emergent behaviors? Can we build systems that preclude some class of emergent behavior (relevant to AGI safety perhaps)? And then of course many systems will not achieve anything because even when "large", they are "uselessly large" - as in, you can define more points on a line and it's still a dumb line.

To me the "how could they not" comes from the idea that if LLMs somehow encapsulate/ contain/ exploit all human writings, then they most likely cover a large part of human intelligence. For damn sure much more than the "basic human". The question is more of how we can get this behavior back out of it - than whether it's there.

Re: Why do LLMs have emergent properties?

#95

Post by kazinator »

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…

> the same loss can be achieved with many different combinations of parameters

Perhaps it can be, but it isn't. The loss in a given model was achieved with that particular combination of parameters that the model has, and there exists no other combination of parameters which which that model can appeal to for more information.

To have the other combinations, we would need to train more models and then somehow combine them so that the combinations are available; but that's just conceptually the same as making one larger model with more parameters, and lower loss.

Re: Why do LLMs have emergent properties?

#96

Post by chii »

Retric wrote: post #79
gyomu wrote: post #68

The reasoning in the article is interesting, but this struck me as a weird example to choose: > “The real question is how can we predict when a new LLM will achieve some new capability X. For example, X = “Write a short story that resonates with the social mood of the present time and is a runaway hit” Framing a capability as something that is objectively measurable (“able to perform math on the 12th grade level”, “a…

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!

Re: Why do LLMs have emergent properties?

#97

Post by Jensson »

andy99 wrote: post #80
PaulDavisThe1st wrote: post #67

Earlier quoted context omitted.

To return an old but still good analogy ... If you want to understand how birds fly, the fact that planes also fly is near useless. While a few common aerodynamic principles apply, both types of flight are so different from each other that you do not learn very much about one from the other. On the other hand, if your goal is just "humans moving through the air for extended distances", it doesn't matter at all that a…

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!

Re: Why do LLMs have emergent properties?

#98

Post by drdeca »

andy99 wrote: post #78
john-h-k wrote: post #77

Earlier quoted context omitted.

> Yes, deep learning models only interpolate What do you mean by this? I don’t think the understanding of LLMs is sufficient to make this claim

An LLM is a classifier, there is lots of research into how deep learning classifiers work, that I haven't seen contradicted when applied to LLMs.

I still think it seems unclear what you mean by “interpolate” in this context? If your NN takes in several numbers and assigns logits to each class based on those numbers, then if you consider the n dimensional space of possible inputs, and if the new input is in the convex hull of the inputs that appear in training samples, then the meaning of “interpolate” is fairly clear.

But when the inputs are sequences of tokens…

Granted, each token gets embedded as some vector, and you can concatenate those vectors to represent the sequence of tokens as one big vector, but, are these vectors for novel strings in the convex hull of such vectors for the strings in the training set?

Re: Why do LLMs have emergent properties?

#99

Post by Jensson »

creer wrote: post #93
teekert wrote: post #39

Perhaps we should ask: Why do humans pick arbitrary points on a continuum beyond which things are labeled “emergent”?

Sure but also see the benchmark creation to benchmark breaking race. The benchmark creation researchers have been doing their best to create difficult, lasting benchmarks that won't be broken by next week. They are not illiterate idiots. And yet the LLMs (or LLM-based systems really) have been consistently so far breaking the benchmarks in absurd time. It's hard to say that nothing significant is going on. Also the f…

> The benchmark creation researchers have been doing their best to create difficult, lasting benchmarks that won't be broken by next week.

That is really easy, say make it play pokemon as well as a 10 year old. That would take a very long time, I watched gemini play pokemon and its nowhere close to that even with all that help.

The hard part isn't making a benchmark that wont be broken, its making a benchmark that is so easy that LLM can solve them but that is still hard for them to solve. Essentially what this means is that we have ran out of easy progress, and are now stumbling in the dark since we have no effective benchmarks to chase.

Re: Why do LLMs have emergent properties?

#100

Post by eutropia »

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.

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