I have a preprint out estimating how many scholarly papers are written using chatGPT etc? I estimate upwards of 60k articles (>1% of global output) published in 2023. https://arxiv.org/abs/2403.16887
How can we identify this? Simple: there are certain words that LLMs love, and they suddenly start showing up *a lot* last year. Twice as many papers call something "intricate", big rises for "commendable" and "meticulous".
I looked at 24 words that were identified as distinctively LLMish (interestingly, almost all positive) and checked their presence in full text of papers - four showed very strong increases, six medium, and two relatively weak but still noticeable. Looking at the number of these published each year let us estimate the size of the "excess" in 2023. Very simple & straightforward, but striking results.
Can we say any one of those papers specifically was written with ChatGPT by looking for those words? No - this is just a high level survey. It's the totals that give it away.
Can we say what fraction of those were "ChatGPT generated" rather than just copyedited/assisted? No - but my suspicions are very much raised.
Isn't this all a very simplistic analysis? Yes - I just wanted to get it out in the world sooner rather than later. Hence a fast preprint.
Is it getting worse? You bet. Difficult to be confident for 2024 papers but I'd wildly guess rates have tripled so far. And it's *March*.
Is this a bad thing? You tell me. If it's a tell for LLM-generated papers, I think we can all agree "yes". If it's just widespread copyediting, a bit more ambiguous. But even if the content is OK, will very widespread chatGPT-ification of papers start stylistically messing up later LLMs built on them? Maybe...
Is there more we could look at here? Definitely. Test for different tells - the list here was geared to distinctive words *on peer reviews*, which have a different expected style to papers. Test for frequency of those terms (not just "shows up once"). Figure out where they're coming from (there seems to be subject variance etc).
Glad I've got something out there for now, though.
huh, this is neat! someone did an AI-detector-tool based analysis looking at preprint platforms, and released it on exactly the same day as mine. Shows evidence for differential effects by discipline & country. https://www.biorxiv.org/content/10.1101/2024.03.25.586710v1
More on LLMs and peer reviews: https://www.404media.co/chatgpt-looms-over-the-peer-review-crisis/
(Back to work tomorrow, & to revising the paper. I feel it's going to be a race to keep up.)
@generalising Gonna be a link in tomorrow morning's ResearchBuzz, too. Thanks! 👍
@researchbuzz great, thankyou!
my work on LLMs has made it to Nature! (Not a sentence I ever expected to write.) https://www.nature.com/articles/d41586-024-01051-2
#bibliometrics #scholcomm #chatgpt