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But that's such a narrow/one dimensional view of how LLMs are used. They can gather data or write an article, but that's probably a minority of use cases.
People have casual conversations with them, code written, brainstorming sessions, dictating a voice-recorded note, and the list goes on.
While data its getting trained on is important, the supposition is that this data consists only of what sits out there on the interwebs.
That as oppose to user input/interaction which, I'm guessing, has a pretty large role in training models. Maybe even more so in some cases than AI-written blog spam.
It's like dictating to a typist like they did in the 60's - he will make sure that your letter looks professional and will fix your grammar, but you will sign the letter. This is totally different from LLM spam, the kind that inflates a sentence into a three-page article full of nothing.
So - is it a problem if the language reverts to a mean? that is the point of a shared language, right?
Which, I mean, fair enough within these constraints, but it's cited like it's a universal law.
Really all that can be taken away from the study is "we trained a very small model on data generated from it in a particular way, and this was eventually harmful for the model."
Also note that models are nowadays trained on massively self-generated data (task RL post-training) and it seems to significantly improve their performance.
I agree - but as the Internet descends into all-slop-all-the-time (seriously, just do a search for reviews or travel advice or technical questions -or most anything - to see it), where do you expect the high quality training material on future things to come from? I have a hard time imagining it.
Textbooks, company wikis, news corpora, structured reports of all kinds from far more sources than what is available on the web.
