The point is that if you made a point to write a completely novel script, with (content-wise, not semantically) 0 DNA in it from previous movie scripts, with an unambiguous but incoherent and unstructured plot, your average literate human would be able summarize what happened on the page, for all that they'd be annoyed and likely distressed by how unusual it was; but that an LLM would do a disproportionately bad job compared to how well they do at other things, which makes us reevaluate what they're actually doing and how they actually do it.
It feels like they've mastered language, but it's looking more and more like they've actually mastered canon. Which is still impressive, but very different.
This tracks, because the entire system reduces to a sophisticated regression analysis. That's why we keep talking about parameters and parameter counts. They're literally talking about the number of parameters that they're weighting during training. Beyond that there are some mathematical choices in how you interrelated the parameters that yields some interesting emergent phenomena, and there are architecture choices to be made there. But the whole thing boils down to regression, and regression is at its heart a development of a canon from a representative variety of examples.
We are warned in statistics to be careful when extrapolating from a regression analysis.
And have you managed to perform such a test or is that just an imaginary result you're convinced will happen ? Not trying to be snarky here but i see this kind of thing a lot and 'this is my model of how LLMs work and so this is how they would behave in this test I cannot verify' is very uncompelling.
It feels like they've mastered language, but it's looking more and more like they've actually mastered canon. Which is still impressive, but very different.