Comment by dist-epoch
1 day ago
> LLM may be a great technology, but it's probably not a good model for how humans learn and use language.
but from all models, it's the closest, "all models are wrong, but some are useful"
1 day ago
> LLM may be a great technology, but it's probably not a good model for how humans learn and use language.
but from all models, it's the closest, "all models are wrong, but some are useful"
> but from all models, it's the closest
Care to back this up? What evidence do we have to suggest that back propagation, self attention, and byte pair encoding are mechanistically similar to human cognition? Similarity of the outputs says nothing about similarity of the machinery underneath. Turboprop engines tell us nothing about how birds fly - doesn't matter that they successfully transport millions of people around the world.
I would say that when it comes to higher level cognition, logic engines are more human like even if they only have a tiny fraction of the practical utility of LLMs.
> Turboprop engines tell us nothing about how birds fly
That is not true.
Turboprop engines and flying birds are different things, but there is a set of common properties.
A blade of a screw propeller functions exactly in the same way as a flapping wing of a bird, except that the wing cannot rotate without limits around the body of the bird, so it must stop, change angles and revert the motion, while the blade can rotate continuously around the axis, functioning on one side like a falling wing and on the other side like a raising wing. The similarities between propeller blades and insect wings are even greater, because insect wings cannot change their shapes so much as those of birds.
For now, our understanding of the networks composed of the neurons of animals is much less advanced than our understanding of wings and propellers, which is why there is a much greater uncertainty about whether there are any similar mechanisms in biological neural networks and in LLMs.
Also, AFAIK, insects rely on their (flat) flapping wings to create lift by creating vortices.
"Nothing" may be a little too strong -- physics is physics, after all -- but (small) birds and airplanes have Reynolds numbers about 1000x different and different underlying material science.
Airplanes are engineered (with a very small number of exceptions) to be stable during flight, whereas bird flight is inherently unstable, requiring a lot of specialized cognition so birds can fly.
I would think a lot of cognitive ornithologists (who I really hope exist!) would be interested in finding out about this.
At this point, I'm at the complete end of my knowledge of bird flight!
We already know that the most basic mechanisms in ANN and actual neurons are very different. Spiking and neurotransmitters are basically everything in brains and don't exist in ANNs.
ok, compare a wheel to a human leg. If you look long enough you will find similarities, but you honestly cannot say that you "understand how humans walk" when all you can do is put a man in a wheelbarrow, move it, and say "look! my device accomplishes the same task!"
> A blade of a screw propeller functions exactly in the same way as a flapping wing of a bird, except that the wing cannot rotate without limits around the body of the bird
The "except" in your argument is doing an incredible amount of work. The rotation of a turboprops generates forward force, wings (whether a bird's or a plane's) generate lift. And plane's wings are called "wings" because they, and not the turboprop, mimic the structure and function of a bird's wings.
Regardless, this whole line of argument is missing the forest for the trees.
attention + residual seems kind of like Chomsky's merge operation.
LLMs behave like they're closest, but only during execution. During training, obviously, they don't resemble the human brain at all. No human reads a million books before they begin to speak.
And I'd be hard pressed to identify many cognitive characteristics of LLMs during execution that resemble human thinking, aside from those that are readily parrotable.
As a model of human cognition, LLMs deep suck.
Humans have billions of years of evolution going for them, that must count…
What reason to we have to believe that LLMs are the closest model we have for how humans learn language.
We considered the hypothesis that humans are a general intelligence that figures language out by exposure. The evidence has consistently come back that that is not how humans actually work. A model where humans are hardwired with a lot of knowledge about language, then fill in the gaps based on exposure still appears to be far more accurate.
The only thing LLMs show is that it is possible to learn language de-novo.
>closest model we have for how humans learn language
Is there some other competing model which can give results as good? If not, the statement seems true in the trivial sense that it's the _only_ model which we've observed to give good results.
People here aren't talking about results, they are talking about the form of the model. Thus the statement you quoted is about how, not about results or outcomes. And it was about how humans learn language, which isn't a result or outcome at all.
> The only thing LLMs show is that it is possible to learn language de-novo.
I agree, but would add, "But not under conditions that humans learn language."
I think it's far too early to say LLMs are not how humans learn language.
Regarding "hard-wired"; it's not if you consider that "hard-wiring" of an individual is the result of millions of years of evolution.