>The output of a GPT is an interpolation (an estimation of new data points inside the range of known data) rather than extrapolation (estimations outside that range).

That's a common meme but it's the opposite of true. Everything big models, not just transformers, mathematically do is extrapolation in the feature space, almost never interpolation. They're perfectly able of combining the ideas, although of course this ability declines once they're off the distribution, just like in humans. The model is creative and its output is transformative, however it only makes sense if you define creativity in a pure manner, based on novelty for itself.

However most people use entirely different definitions of creativity, something like "surprise me in a way that still makes sense to me". This includes "me", a side observer, and depends on what the side observer considers novel. Hence the main reason for the lack of "creativity" in big models is not some hand-waved "regression to the mean" or "interpolation", but the fact that they're still insufficiently intelligent compared to a human. Current models simply don't have enough fidelity to understand humans and think as deep, that's why humans think their output isn't sufficiently novel for them! Think about this, insanely smart aliens would probably dismiss humans as non-creative too.

The contributing factor is also the lack of semantic diversity in current models. Also known as mode collapse, but the name is a bit of misnomer and describes a technicality, not the resulting phenomenon. This indirectly affects creativity as it's usually understood, because the models generate and repeat the same thing in response to the same thing, which is the opposite of novel in layman's definition. That's part of where the slop comes from. Mode collapse has many causes, e.g. post-training with current algorithms. It's likely fixable but AI shops show little to no interest in studying and fixing it.

> That's a common meme but it's the opposite of true.

But you're restating what I just wrote - We're training a status quo machine and the probability of anything outside that distribution rapidly drops to zero.

so you're saying the difference b/w extrapolation and interpolation is subjective unless the difference is defined tautologically?