Well, like I was hinting at above, it's actually based on a close analysis of all the ways it doesn't quack like a duck, together with some serious consideration of how plausible I believe it is that a decoder architecture would be capable of performing generic symbolic reasoning tasks. The crux of the problem is that general reasoning involves actively selecting and then manipulating a symbolic model at least semi-independently of language generation, and that's a very different shape of information flow from next token prediction, even with MHA.

I would certainly be open to hearing a detailed explanation of how that kind of thing might actually support reasoning, but, absent that, pointing out that I don't understand how brains do it (which is true) is the pot calling the kettle black.

I actually want to turn the tables on your claim about humans being on a pedestal, and this is another spot where I believe I side with Hofstadter. We humans have a tendency to believe that certain behaviors are so special that the only way to accomplish a similar outwardly-visible behavior is by using similar underlying mechanisms. I see a healthy measure of chauvinism in that assumption.

As I mentioned earlier, these LLMs are trained on vast collections of both the inputs and outputs of genuine human reasoning, and potentially have enough parameters to record a large proportion of that information in the form of probability distributions. And even something as crude as a singular value decomposition on a term-context matrix is enough to produce generalizations that rather spookily resemble human semantic models. Neural models do the same thing much better, but I'm still not prepared to see much in that beyond more evidence in favor of the distributional hypothesis. Which means that there's plenty of room to believe that what seems like reasoning on the part of the LLM is actually more akin to a projection from a sort of "holographic" representation of written artifacts of human reasoning.

That's not placing humans on a pedestal. That's just thinking hard about possible explanations for a phenomenon and then provisionally choosing the one I believe is most parsimonious. As any believer in scientific skepticism should strive to do.

>Well, like I was hinting at above, it's actually based on a close analysis of all the ways it doesn't quack like a duck

I doubt that, if your descriptions are anything to go by. "appropriately applies syllogism to arrive at correct conclusions" is just not something humans reliably do, so if that is the bar here then it's not worth anything, no matter what secret benchmark you think you've cooked up. Will you say humans cannot reason as well?

There's the classic example of the Wason Selection Task. Most people fail a simple conditional reasoning problem unless it’s dressed up in familiar social context, like catching cheaters.

>I would certainly be open to hearing a detailed explanation of how that kind of thing might actually support reasoning, but, absent that, pointing out that I don't understand how brains do it (which is true) is the pot calling the kettle black.

Not really. You're the one making a positive mechanistic claim here: that reasoning requires "actively selecting and manipulating a symbolic model at least semi independently of language generation" and that a decoder transformer therefore probably isn't doing it. The claim needs justification and it is once again another example of you assuming your claim and working backwards towards it.

"Next token prediciton" describes the training objective and the interface through which the model's computation is expressed. It does not tell you what computation the network must perform internally to minimize that objective. There is nothing contradictory about a system learning internal representations, manipulating them, and then using the result to predict the next token.

In fact, we already know transformers are capable of considerably richer internal computation than your description suggests. Much richer than we can even begin to understand.

We don't possess some mechanistic definition of human reasoning which we've opened the human skull and verified. We infer that humans reason largely from behaviour.

So if some other system exhibits some of the same behaviours, you need some non-question-begging criterion for why they cease to count as reasoning in that system. Otherwise the position just becomes unfalsifiable: when the model succeeeds on a novel reasoning problem, that's merely a "projection from a holographic representaion" (this is a meaningless statement by the way); when it fails, then you jump to claiming its evidence that wasn't/cannot reason at all (which is just silly and bad science).

>I actually want to turn the tables on your claim about humans being on a pedestal, and this is another spot where I believe I side with Hofstadter. We humans have a tendency to believe that certain behaviors are so special that the only way to accomplish a similar outwardly-visible behavior is by using similar underlying mechanisms. I see a healthy measure of chauvinism in that assumption.

Nobody is requiring the same mechanism. That's my point. I'm perfectly happy for transformers to reason in a drastically different way from humans (though i suspect it's not so drastic). You are the one requiring that it must have a particular internal form - one that cannot even be validated to exist - and then rejecting systems whose architecture doesn't obviously resemble that form.

>As I mentioned earlier, these LLMs are trained on vast collections of both the inputs and outputs of genuine human reasoning, and potentially have enough parameters to record a large proportion of that information in the form of probability distributions. And even something as crude as a singular value decomposition on a term-context matrix is enough to produce generalizations that rather spookily resemble human semantic models. Neural models do the same thing much better, but I'm still not prepared to see much in that beyond more evidence in favor of the distributional hypothesis. Which means that there's plenty of room to believe that what seems like reasoning on the part of the LLM is actually more akin to a projection from a sort of "holographic" representation of written artifacts of human reasoning.

That doesn't distinguish reasoning frm 'non reasoning'. Learning from artifacts of reasoning is perfectly compatible with learning procedures that reproduce the underlying structure which generated those artifacts.

Appealing to the distributional hypothesis doesnt help much either. The fact that relatively simple statistical methods can recover meaningful latent structure from text shows that structure is present in the data; it does not establish that a much more powerful model trained on the data can do nothing beyond "projecting" stored patterns.

>That's not placing humans on a pedestal. That's just thinking hard about possible explanations for a phenomenon and then provisionally choosing the one I believe is most parsimonious. As any believer in scientific skepticism should strive to do.

Your explanantion isn't more parsimonious simply because you labelled all succesful reasining like behaviour "projection". All you've done is add an unobserved distinction. It's a meaningless semantic game. You call it a projection because you want to believe LLMs don;t reason, not because it's a meaningfull term that adds some helpful falsifiable criteria.