Whether the information in the paper can be trusted is an entirely separate concern.
Old Chinese mathematics texts are difficult to date because they often purport to be older than they are. But the contents are unaffected by this. There is a history-of-math problem, but there's no math problem.
You are totally correct that hallucinated citations do not invalidate the paper. The paper sans citations might be great too (I mean the LLM could generate great stuff, it's possible).
But the author(s) of the paper is almost by definition a bad scientist (or whatever field they are in). When a researcher writes a paper for publication, if they're not expected to write the thing themselves, at least they should be responsible for checking the accuracy of the contents, and citations are part of the paper...
Problem is that most ML papers today are not independently verifiable proofs - in most, you have to trust the scientist didn't fraudulently produce their results.
There is so much BS being submitted to conferences and decreasing the amount of BS they see would result in less skimpy reviews and also less apathy
Not really true nowadays. Stuff in whitepapers needs to be verifiable which is kinda difficult with hallucinations.
Whether the students directly used LLMs or just read content online that was produced with them and cited after just shows how difficult these things made gathering information that's verifiable.
Old Chinese mathematics texts are difficult to date because they often purport to be older than they are. But the contents are unaffected by this. There is a history-of-math problem, but there's no math problem.