>Comprehension requires deriving meaning, and GPT doesn't engage with meaning at all. It predicts which word is most likely to come next in a sequence, but that's it.
Why think that "engaging with meaning" is not in the solution-space of predicting the next token? What concept of meaning are you using?
You could argue that GPT has a model of meaning somewhere inside of it, but that's besides the point. If that meaning is hiding latent inside GPT, then it's not accessible to any other system which might want to use GPT as an interface. GPT accepts English as input and produces English as output; that's it.
That said, no, I don't think GPT properly grasps meaning, and my reason for that is simple: It regularly contradicts itself. It can put together words in a meaningful-looking order, but if it actually understood what those words meant as an emergent property of its design, then you wouldn't be able to trick it into saying things that don't make sense or are contradictory. If someone actually understands a subject, they won't make obvious mistakes when they discuss it; since GPT makes obvious mistakes, it can't actually grasp meaning—only brush up against it.
>since GPT makes obvious mistakes, it can't actually grasp meaning—only brush up against it.
This argument doesn't apply to GPT because it isn't a single coherent entity with a single source of truth. GPT is more like a collection of personas, which persona you get is determined by how you query it. One persona may say things that contradict other personas. Even within personas you may get contradictory statements because global consistency is not a feature that improves training performance, and can even hinder it. Samples from its training data are expected to be inconsistent.
It is important not to uncritically project expectations onto LLMs derived from our experiences with human agents. Their architecture and training regime is vastly different than humans and so we should expect their abilities to manifest differently than analogous abilities in humans. We can be easily mislead if we don't modify our expectations for this alien context.
I get what you mean here but they probably mean referential meaning... having never seen a dog, GPT doesn't really know what a dog is on a physical level, just how that word relates to other words.
Why think that "engaging with meaning" is not in the solution-space of predicting the next token? What concept of meaning are you using?