When some closed models are "retired", interestingly we might not even be able to extract all their tells. We'd be limited to people writing about them on forums (where they don't often specify version, and there's a lot of memes and confirmation bias) and I don't know, doing some statistical Bayesian guessing that given the likelihood it's FooGPT 10.5, maybe we should update our beliefs about its characteristics etc.

> There are a finite number of words and a finite way of combining them within an academic setting/field which means that we can't build a perfect classifier with just text.

Maybe for very short phrases, but otherwise I disagree. Phrasing very quickly runs into a combinatorial explosion. In the words of Noam Chomsky, "Virtually every sentence that a person utters or understands is a brand-new combination of words, appearing for the first time in the history of the universe."

In my opinion, the difficulty in LLM/human text discrimination isn't that a person might coincidentally write exactly the same text as an LLM would, but rather that 1) LLMs aren't hard locked to a single phrasing (so this is a tougher problem than matching to a single static document, e.g. plagiarism detection) and 2) text has relatively low information density, so you need quite a bit of it to gather enough data to run a statistical test with a reasonably narrow confidence interval.