The entire paper is almost certainly Claude-generated, given the clear Claude-speak throughout. It seems like they didn’t even try to have Claude write a polished paper: it reads like Claude writing up a lengthy report, down to the needless sectioning with idiosyncratic title language. There appears to be no disclosure, and the author contribution section appears to falsely claim that a particular author wrote the text.
I know arXiv has taken some measures to combat spam like this, but it seems like they’ll need to do more. There’s just very little barrier now to creating giant slop papers like this and then dumping them anywhere that won’t reject them. It is an insult to everyone’s time, and I can’t imagine they expect people to actually read this. If the expectation is that everyone will use an LLM to interpret it, then maybe they should have at least had a few more rounds of tightening and polishing the paper, even via LLM, to save the redundant token use.
Not sure about the paper but the results make sense, we see it in PDF extraction too. Fields that aren't in the document are being made up 11 to 40% of the time depending on the API.
The whole thing is nasty partly because it isn't just AI problem. When checking the human labels that we used in our evals 40 out of 142 answer keys claiming absence were wrong. Tricky one.
May be AI written. Used my AI to get the gist of it. One thing that I believe is LLMs need to be considered a pure play tool and can be guided to point out misses as well. The reason is simple. To find whats missing, one needs to ask the right questions on how to judge and when that is there.
The fact that even the abstract is very clearly AI-generated does not fill me with confidence in the rigour of the research.
"Verify Presence, Not Absence" is very LLM-coded all on its own.
I understand the answer to the question I'm about to ask, but how does any human read that abstract and not think, "This is entirely too many colons."
EDIT: And Pangram agrees that the abstract is 100% AI generated.
While I agree I have to say that AI-detectors are complete bullshit
Interesting. My initial thought was “this is so horribly written it must be human”
The entire paper is almost certainly Claude-generated, given the clear Claude-speak throughout. It seems like they didn’t even try to have Claude write a polished paper: it reads like Claude writing up a lengthy report, down to the needless sectioning with idiosyncratic title language. There appears to be no disclosure, and the author contribution section appears to falsely claim that a particular author wrote the text.
I know arXiv has taken some measures to combat spam like this, but it seems like they’ll need to do more. There’s just very little barrier now to creating giant slop papers like this and then dumping them anywhere that won’t reject them. It is an insult to everyone’s time, and I can’t imagine they expect people to actually read this. If the expectation is that everyone will use an LLM to interpret it, then maybe they should have at least had a few more rounds of tightening and polishing the paper, even via LLM, to save the redundant token use.
I had to use an AI to undertand the abstract made by AI…
Not sure about the paper but the results make sense, we see it in PDF extraction too. Fields that aren't in the document are being made up 11 to 40% of the time depending on the API.
The whole thing is nasty partly because it isn't just AI problem. When checking the human labels that we used in our evals 40 out of 142 answer keys claiming absence were wrong. Tricky one.
May be AI written. Used my AI to get the gist of it. One thing that I believe is LLMs need to be considered a pure play tool and can be guided to point out misses as well. The reason is simple. To find whats missing, one needs to ask the right questions on how to judge and when that is there.
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