I’m no LLM expert but I think there is a distinction to be made between the LLM dataset and the output it gives to the user. What you’re suggesting is that it’s difficult to remove something from the dataset, which may be the case. But that doesn’t mean the user will necessarily be able to access it.

My guess is that this is much easier to attack from the user end.

The "reasonable" singular removal is more about coming up with ways to block prompts that can produce infringing content, and having filters on the other end to catch infringements before they are published to the user. It's an endless whack-a-mole that never actually addresses the problem but might look good enough to the legal system or to keen supporters.

Barring some major breakthrough, the actual answer is to train a new model without the infringing data.

I think some of the people saying "remove it from your model" are aware of this and are simply being glib and needling; "you've created this infringement monstrosity, so surely you made sure to include a way to deal with this problem without throwing away all of your work, right?"

I think given the use of ply for AlphaGo/chess engines this is a pretty cool metaphor.