this post was submitted on 23 Dec 2023
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[–] [email protected] 1 points 10 months ago (3 children)

Unless you want to call your predictive text on your keyboard a mind you really can't call an LLM a mind. It is nothing more than a linear progression from that. Mathematically proven to not show any form of emergent behavior.

[–] [email protected] 4 points 10 months ago (1 children)

No such thing has been "mathematically proven." The emergent behavior of ML models is their notable characteristic. The whole point is that their ability to do anything is emergent behavior.

[–] [email protected] 0 points 10 months ago* (last edited 10 months ago) (1 children)

Here's a white paper explicitly proving:

  1. No emergent properties (illusory due to bad measures)
  2. Predictable linear progress with model size

https://arxiv.org/abs/2304.15004

The field changes fast, I understand it is hard to keep up

[–] [email protected] 2 points 10 months ago (1 children)

Sure, if you define "emergent abilities" just so. It's obvious from context that this is not what I described.

[–] [email protected] -1 points 10 months ago (1 children)

Their paper uses industry standard definitions

[–] [email protected] 2 points 10 months ago

Their paper uses terminology that makes sense in context. It's not a definition of "emergent behavior."

[–] [email protected] 3 points 10 months ago* (last edited 10 months ago) (1 children)

I do not think that it is “linear” progression. ANN by definition is nonlinear. Neither I think anything is “mathematically proven”. If I am wrong, please provide a link.

[–] [email protected] 1 points 10 months ago (1 children)

Sure thing: here's a white paper explicitly proving:

  1. No emergent properties (illusory due to bad measures)
  2. Predictable linear progress with model size

https://arxiv.org/abs/2304.15004

[–] [email protected] 2 points 10 months ago* (last edited 10 months ago)

Thank you. This paper though does not state that there are no emergent abilities. It only states that one can introduce a metric with respect to which the emergent ability behaves smoothly and not threshold-like. While interesting, it only suggests that things like intelligence are smooth functions, but so what? Some other metrics show exponential or threshold dependence and whether the metric is right depends only how one will use it. And there is no law that emerging properties have to be threshold like. Quite the opposite - nearly all examples in physics that I know, the emergence appears gradually.

[–] [email protected] 0 points 10 months ago (1 children)

It is obvious that you do not know what either "mathematical proof" or "emergence" mean. Unfortunately, you are misrepresenting the facts.

I don't mean to criticize your religious (or philosophical) convictions. There is a reason people mostly try to keep faith and science separate.

[–] [email protected] 0 points 10 months ago (1 children)

Here's a white paper explicitly proving:

No emergent properties (illusory due to bad measures)

Predictable linear progress with model size

https://arxiv.org/abs/2304.15004

The field changes fast, I understand it is hard to keep up

[–] [email protected] 0 points 10 months ago (1 children)

As I said, you do not understand what these 2 terms mean. As such, you are incapable of understanding that paper.

Perhaps your native language is Italian, so here are links to the .it Wikipedia.

https://it.wikipedia.org/wiki/Comportamento_emergente

https://it.wikipedia.org/wiki/Dimostrazione_matematica

[–] [email protected] 1 points 10 months ago (1 children)
  1. Emergence is the whole being greater than the sum of its parts. That's the original meaning of emergent properties, which is laid out in the first paragraph of the article. It's the scholarly usage as well, and what the claims of observed emergence are using as the base of their claim.

  2. The article very explicitly demonstrated that only about 10% of any of the measures for LLMs displayed any emergence and that illusory emergence was the result of overly rigid metrics. Swapping to edit distance as an approximately close metric causes the sharp spikes to disappear for obvious reasons: no longer having a sharp yes/no allows for linear progression to reappear. It was always there, merely masked by flawed statistics.

If you can't be bothered to read here's a very easy to understand video by one of the authors: https://www.youtube.com/watch?v=ypKwNrmuuPM

[–] [email protected] 0 points 10 months ago

Good. Now do you understand how you have misrepresented the paper?