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this post was submitted on 29 Feb 2024
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I really don't think your analogy is a great one there. We can't compare brains to computers usefully because they're super distinct. You're sneaking in this assumption that there is more complexity to the models by implying there's something larger present being terminated early but there isn't.
This seems as absurd to me as asking whether a clock has a concept of time. Being very good at doing time related stuff, vastly superior to a human, is not evidence in favour of having any sort of knowledge of time. I think that the interface of these models may be encouraging you to attribute more to them than there could possibly be.
The analogy is only there to point out the flaw in your thinking, the lack of persistence applies to both humans (if we shoot them quickly) and LLMs and so your argument applies in both cases. And I can do the very same trick to the clock analogy. You want to say that a clock is designed to keep time and that's all it does therefore it can't understand time. But I say, look, the clock was designed to keep time yes but that is far from all it does, it also transforms electrical energy into mechanical and uses it to swing around some arms at constant speed, and we can't see the inside of the clock who knows what is going on in there, probably nothing that understands the concept of time but we'd have to look inside and see. LLMs were designed to predict the next token, they do actually do so, but clearly they can do more than that, for example they can solve high school level math problems they have never seen before and they can classify emails as being spam or not. Yes these are side effects of their ability to predict token sequences as human reasoning is a side effect of their ability to have lots of children. The essence of a task is not necessarily the essence of the tool designed specifically for that task.
If you believe LLMs are not complex enough to have understanding and you say that head on I won't argue with you, but you're claiming that their architecture doesn't allow it even in theory then we have a very fundamental disagreement
Huh? a human brain is a complex as fuck persistent feedback system. When a nervous impulse starts propagating through the body/brain whether or not that one specifically has time to be integrated into consciousness has no bearing on the existence of a mind that would be capable of doing so. It's not analogous at all.
No see this is where we're disagreeing. They can output strings which map to solutions of the problem quite often. Because they have internalised patterns, they will output strings that don't map to solutions other times, and there is no logic to the successes and failures that indicate any sort of logical engagement with the maths problem. It's not like you can say "oh this model understands division but has trouble with exponentiation" because it is not doing maths. It is doing string manipulation which sometimes looks like maths.
This is reductive to the point of absurdity. you may as well say human reasoning is a side effect of quark bonding in rapidly cooling highly localised regions of space time. you won't actually gain any insight by paving over all the complexity.
LLMs do absolutely nothing like an animal mind does, humans aren't internalising massive corpuses of written text before they learn to write. Babies learn conversation turn taking long before anything resembling speech for example. There's no constant back and forth between like the phonological loop and speech centers as you listen to what you just said and make the next sound.
The operating principle is entirely alien and highly rigid and simplistic. It is fascinating that it can be used to produce stuff that often looks like what a conscious mind would do but that is not evidence that it's doing the same task. There is no reason to suspect there is anything capable of supporting understanding in an LLM, they lack anything like the parts we expect to be present for that.
Every time-limited feedback system is entirely equivalent to a feed-forward system, similar to how you can unroll a for loop.
String manipulation and computation are equivalent, do you think not just LLMs but computers themselves cannot in principal do what a brain does?
No because that has nothing to do with the issue at hand. Humans and LLMs and rocks all have this in common. What humans and LLMs do have in common is that they are a result of an optimization process and do things that weren't specifically optimized for as side effects. LLMs probably don't understand anything but certainly it would help them to predict the next token if they did understand, describing them as only token predictors doesn't help us with the question of whether they have understanding.
Again, I am not trying to argue that LLMs are like people or that they are intelligent or that they understand, I am not trying to give evidence of this. I'm trying to show that this reasoning (LLMs merely predict a distribution of next tokens -> LLMs don't understand anything and therefore can't do certain things) is completely invalid