51–60 of 121 posts
The authors haven’t demonstrated emergence of LLMs. If I write a piece of code and it does what I programmed it to do that’s not emergence. LLMs aren’t doing anything unexpected yet. I think that’s the smell test because emergence is still subjective.
Earlier quoted context omitted.
I've always understood it more to mean, "phenomena that happen due to the interactions of a system's parts without being explicitly encoded into their individual behavior." Fractal patterns in nature are a great example of emergent phenomena. A single water molecule contains no explicit plan for how to get together with its buddies and make spiky hexagon shapes when they get cold. And I've always understood talking a…
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…
Metaphor: finding a path from a initial point to a destination in a graph. As the number of parameters increases one can expect the LLM to be able to remember how to go from one place to another and in the end it should be able to find a long path. This can be an emergent property since with less parameters the LLM could not be able to find the correct path. Now one has to find what kind of problems this metaphor is…
Earlier quoted context omitted.
I've always understood it more to mean, "phenomena that happen due to the interactions of a system's parts without being explicitly encoded into their individual behavior." Fractal patterns in nature are a great example of emergent phenomena. A single water molecule contains no explicit plan for how to get together with its buddies and make spiky hexagon shapes when they get cold. And I've always understood talking a…
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…
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?
Earlier quoted context omitted.
Not at all. Here is an analogy: A car is a system which brings you from point A to B. No part of the car can bring you from point A to B. Not the seats, the wheels, not the frame, not even the motor. If you put the motor on a table, it won’t move one bit. The car, as a system, however does. The emergent property of a car, seen as a system, is that it brings you from one location to another. A system is the product of…
That sounds like semantics. If putting together a bunch of X's in a jar always makes the jar go Y, then is Y an emergent property? Or we need to better understand why a bunch of X's in a jar do that, and then the property isn't emergent anymore, but rather the natural outcome of well-understood X's in a well-understood jar.
As in your example: If a bunch of x in a jar leads to the jar tipping over, it is not emergent. That’s just cause and effect. Problem to start with is that the jar containing x is not even a system in the first place, emergence as a concept is not applicable here.
There may be a misunderstanding on your side of the term emergence. Emergence does not equal non-understanding or some spooky-hooky force coming from the unknown. We understand the functions of the elements of a car quite well. The emergent behaviour of a car was intentionally brought about by massive engineering.
Reductionism does not lead to an explaining-away of emergence.
Alternate view: Are Emergent Abilities of Large Language Models a Mirage? https://arxiv.org/abs/2304.15004 "Here, we present an alternative explanation for emergent abilities: that for a particular task and model family, when analyzing fixed model outputs, emergent abilities appear due to the researcher's choice of metric rather than due to fundamental changes in model behavior with scale. Specifically, nonlinear or…
To me the paper is overhyped. Knowing how neural networks work, it's clear that there are going to be underlying properties that vary smoothly. This doesn't preclude the existence of emergent abilities.
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?
I'm not a techie, so perhaps someone can help me understand this: AFAIK, no theoretical computer scientist predicted emergence in AI models. Doesn't that suggest that the field of theoretical computer science (or theoretical AI, if you will) is suspect? It's like Lord Kelvin saying that heavier-than-air flying machines are impossible a decade before the Wright brothers' first flight.