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I think these sort of efforts are mostly self-soothing at this point. It is almost certainly the case that the labs are at a minimum running inference over the information they're pulling and ensuring that it's useful/suitable for pre-training. The models are at least good enough to know whether they're looking at utter nonsense.

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Ya I feel like these AI companies have the ability to be somewhat selective about their training sets. They don't have to add everything. I guess the idea is the filters wouldn't catch it, but if the junk is indistinguishable from the real stuff, then won't the platforms just be ruined by a bunch of junk?

Actually it was shown a couple of times already, some of it also by Anthropic's own research, that the LLMs are extremely easy to poison with small datasets.

That's correct, and their recent work on natural language autoencoders has given extremely compelling evidence of that...which is why their data collection practices for pre-training have almost certainly evolved, particularly since they've already scraped most of the internet.

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