this post was submitted on 28 Jul 2023
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I don't see how that affects my point.
So at any point in time, only recent text could be "contaminated". The claim that "all text after 2023 is forever contaminated" just isn't true. Researchers would simply have to be a bit more careful including it.
Your assertion that a future AI detector will be able to detect current LLM output is dubious. If I give you the sentence "Yesterday I went to the shop and bought some milk and eggs." There is no way for you or any detection system to tell if that was AI generated or not with any significant degree of certainty. What can be done is statistical analysis of large data sets to see how they "smell", but saying around 30% of this dataset is likely LLM generated does not get you very far in creating a training set.
I'm not saying that there is no solution to this problem, but blithely waving away the problem saying future AI will be able to spot old AI is not a serious take.
If you give me several paragraphs instead of a single sentence, do you still think it's impossible to tell?
What they're getting towards (one thing, anyways) is that "indistinguishable to the model" and "the same" are two very different things.
IIRC, one possibility is that LLMs which learn from one another will make such incremental changes to what's considered "acceptable" or "normal" language structuring that, over time, more noticeable linguistic changes begin to emerge that go unnoticed by the models.
As it continues, this phenomena creates a "positive feedback loop" in which the gap progressively widens -- still undetected, because the quality of training data is going down -- to the point where models basically "collapse" in their effectiveness.
So even if their output is indistinguishable now, how the tech is used (I guess?) will determine whether or not a self-destructive LLM echo chamber is produced.