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Comment by bonsai_spool | Hacker News Reader

This suggests that nobody was screening this papers in the first place—so is it actually significant that people are using LLMs in a setting without meaningful oversight?

These clearly aren't being peer-reviewed, so there's no natural check on LLM usage (which is different than what we see in work published in journals).

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As one who reviews 20+ papers per year, we don't have time to verify each reference.

We verify: is the stuff correct, and is it worthy of publication (in the given venue) given that it is correct.

There is still some trust in the authors to not submit made-up-stuff, albeit it is diminishing.

I'm surprised the conference doesn't provide tooling to validate all references automatically.

How would you do that? Even in cases where there's a standard format, a DOI on every reference, and some giant online library of publication metadata, including everything that only exists in dead tree format, that just lets you check whether the cited work exists, not whether it's actually a relevant thing to cite in the context.

Sorry, but if someone makes a claim and cites a reference, how do you verify "is the stuff correct" without checking that reference?

Those are typically things you are familiar with or can easily check.

Fake references are more common in the introduction where you list relevant material to strengthen your results. They often don't change the validity of the claim, but the potential impact or value.

Academic venues don't have enough reviewers. This problem isn't new, and as publication volumes increase, it's getting sharply worse.

Consider the unit economics. Suppose NeurIPS gets 20,000 papers in one year. Suppose each author should expect three good reviews, so area chairs assign five reviewers per paper. In total, 100,000 reviews need to be written. It's a lot of work, even before factoring emergency reviewers in.

NeurIPS is one venue alongside CVPR, [IE]CCV, COLM, ICML, EMNLP, and so on. Not all of these conferences are as large as NeurIPS, but the field is smaller than you'd expect. I'd guess there are 300k-1m people in the world who are qualified to review AI papers.

Seems like using tooling like this to identify papers with fake citations and auto-rejecting them before they ever get in front of a reviewer would kill two birds with one stone.

It's not always possible to distinguish between fake citations and citations that are simply hard to find (e.g. wonderful old books that aren't on the Internet).

Another problem is that conferences move slowly and it's hard to adjust the publication workflow in such an invasive way. CVPR only recently moved from Microsoft's CMT to OpenReview to accept author submissions, for example.

There's a lot of opportunity for innovation in this space, but it's hard when everyone involved would need to agree to switch to a different workflow.

(Not shooting you down. It's just complicated because the people who would benefit are far away from the people who would need to do the work to support it...)

Sure, I agree that it's far from trivial to implement.

When I was reviewing such papers, I didn't bother checking that 30+ citations were correctly indexed. I focused on the article itself, and maybe 1 or 2 citations that are important. That's it. For most citations, they are next to an argument that I know is correct, so why would I bother checking. What else do you expect? My job was to figure out if the article ideas are novel and interesting, not if they got all their citations right.

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