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".
@generalising but if LLMs derive their models from papers that are already published, how can there be massive increases in such words? I like the idea, but this sounds strange.
@Wikisteff Credit where it's due - I took the sample list from an earlier study! https://arxiv.org/abs/2403.07183 (p 15, 16) I think this is a bit of an idiosyncratic list due to the peer-review context (hence it's all adjectives/adverbs, almost all positive) and there will definitely be other distinctive terms, some unpredictable - it would be quite interesting to do some larger analysis to try and find them.
@generalising It's a fantastic idea!
I used fine-grained stylometrics to identify the unique-ish "fists" of posters and their proxy accounts in Twitter posts in 2022 to do some hypothesis testing of co-authorship amongst accounts in the aftermath of the 2022 Convoy Protest here in Ottawa, but I hadn't thought of using them for bibliometrics and AI!
It's a genius move! :)
@mirgray There's a LOT you can do with stylometrics. I'm still kind of hoping that LLMs can be used to identify the fists of individual authors in their training data reliably, as clearly the data are in there ("please write a sonnet about how bias in decision AIs is a challenging issue in the style of William Shakespeare").
@generalising
Very cool!
from what I understand, if a paper contains both "“intricate” and “meticulous” or
“intricate” and “notable” there is a very high chance it is by LLM (although, of course that's not a proof..especially now that your work will be noticed).
Is there hope to get automatic LLM with some~99% confidence? Of course the entire point is that LLM mimic human language, but you are showing that language correlations appear and are strong indicators, and they may hard to polish.
@franco_vazza there's always a baseline of usage, but that pair together has suddenly become a lot more common.
I don't think this approach is great for detecting LLM involvement in any individual paper (there are much more sophisticated tools for that) but it works OK for estimation at a much broader scale.
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