I wonder how many of the GPT-generated papers are actually made by people whose native language is not English and who want to improve their English. That would explain various "as of my last knowledge update" still left intact in the papers, if the authors don't fully understand what it means.
I'm guessing that we don't want people to write papers in a language where they don't understand "as of my last knowledge update", as probably a lot of terms in their paper have more advanced language than that.
Would be better in those cases for people to write their paper in their native language and let readers translate it for themselves.
GPT might make fabricating scientific papers easier, but let's not forget how many humans fabricated scientific research in recent years - they did a great job without AI! For any who haven't seen/heard, this makes for some entertaining and eye-opening viewing! https://www.youtube.com/results?search_query=academic+fraud
Difficulty and scale matter where it comes to fabrication.
Academia is a lot about barriers, which while sometimes unpleasant and malfunctioning nevertheless serve a purpose (unfortunately, it is impossible to evaluate everything fully on per-case basis, so humans need shortcuts to filter out noise and determine quicker if it is worth spending attention on). One of the barriers is in the form of the paper itself. The fall of this barrier (notably through often unauthorised use of others’ IP) would likely bring about not sudden idyllic meritocracy but increased noise and/or strengthening of other barriers.
How can I trust the paper when there is no proper proofreading?
How do you know there is no proper proofreading? There is no way to tell, is there? Just because content was generated by an LLM doesn't in itself mean that it wasn't proofread.
> Methods
> We searched and scraped Google Scholar using the Python library Scholarly (Cholewiak et al., 2023) for papers that included specific phrases known to be common responses from ChatGPT and similar applications with the same underlying model (GPT3.5 or GPT4): “as of my last knowledge update” and/or “I don’t have access to real-time data” (see Appendix A).
If noone bothered to even spot and remove these, you can be pretty sure that no human ever read the whole paper before publication.
I appreciate that, appropriately, the article image is not AI-generated.
It's silly that there's a stigma attached to AI generated images in cases where it's perfectly reasonable to do. People seem to appreciate things more for the fact that they were created by spending time out of another human's life more than what it actually is.
It would be silly if they were indistinguishable from human-created images, but they aren't, exhibiting the typical AI artifacts and weirdness, and thereby signal a lack of care/caring.
GPT might make fabricating scientific papers easier, but let's not forget how many humans fabricated scientific research in recent years - they did a great job without AI! For any who haven't seen/heard, this makes for some entertaining and eye-opening viewing! https://www.youtube.com/results?search_query=academic+fraud
Sure, but that takes time, AI has the potential to generate “real sounding”papers in under a second. At least the fake papers before were rate limited.
I wonder how many of the GPT-generated papers are actually made by people whose native language is not English and who want to improve their English. That would explain various "as of my last knowledge update" still left intact in the papers, if the authors don't fully understand what it means.
I'm guessing that we don't want people to write papers in a language where they don't understand "as of my last knowledge update", as probably a lot of terms in their paper have more advanced language than that. Would be better in those cases for people to write their paper in their native language and let readers translate it for themselves.
It’s not a black and white problem. Some people may have good ability to read but not write/speak a language (I’m that way with Spanish) so the cases will vary as to which would work best user or author translated, it could be good to include both version in any given paper and fix both problems.
You can also get reasonably close with an open model that you can run locally (flux dev). https://replicate.com/p/xm41nvz05drm00chsywb6am7f0 https://replicate.com/p/kdw8bnkj39rm40chsyzbyg5e04 But of course anyone who has even a passing familiarity with scrabble is going to be able to tell that something's off.
The biggest problem with the default Flux model is that it generates images with that strong AI look, probably caused by the distillation of the CFG. You should try some LoRAs for this, and also prompt the model to generate the rack that holds the letters.
Good point. I have a comfyui setup for it but its super basic right now just the diffusion model / clip loader / vae. Another thing you've probably noticed is that 99% of images from Flux tend to have that classic narrow depth of field look. I've seen people occasionally be able to get around it with pretty amusing prompt tokens like "instagram photo, selfie, gopro, etc." though.
Writing consists of iterated re-writing (to me, anyways), i.e. better and better ways to express content 1. correctly, 2. clearly and 3. space-economically. By writing it down (yourself) you understand what claims each piece of related work discussed has made (and can realistically make - as there sometims are inflationary lists of claims in papers), and this helps you formulate your own claim as it relates to them (…
Does every researcher write summaries of related research themselves?
Pretty much yes. Critical analysis is a necessary skill that needs practice. It's also necessary to be aware of the intricacies of work in one's own topic area, defined narrowly, to clearly communicate how one's own methods are similar/different to others' methods.
GPT might make fabricating scientific papers easier, but let's not forget how many humans fabricated scientific research in recent years - they did a great job without AI! For any who haven't seen/heard, this makes for some entertaining and eye-opening viewing! https://www.youtube.com/results?search_query=academic+fraud
I think it’s important to remember that while the tidal wave of spam just starting to crest courtesy of the less scrupulous LLM vendors is uh, necessary to address, this century’s war on epistemology was well underway already in the grand traditions of periodic wars on the idea that facts are even aspirationally, directionally worthwhile. The phrase “alternative facts” hit the mainstream in 2016 and the idea that res…
>(that was around the time I was starting to feel ill for being a key architect of it).
Hmm there may be a bug in the authors’ python script that searches google scholar for the phrases "as of my last knowledge update" or "I don't have access to real-time data". You can see the code in appendix B. The bug happens if the ‘bib’ key doesn’t exist in the api response. That leads to the urls array having more rows than the paper_data array. So the columns could become mismatched in the final data frame. It s…
As a tangent to the paper topic itself, what should be the standard procedure for publishing data gathering code like this? Given that they don't specify which version of any libraries or APIs used and that updates occur over time, API's change etc. inevitably resulting in code rot. It will eventually be impossible to figure out exactly what this code did. With meticulous version records it should at least be possibl…
In my opinion, archive the data that was actually gathered and the code's intermediate & final outputs. Write the code clearly enough that what it did can be understood by reading it alone, since with pervasive software churn it won't be runnable as-is forever. As a bonus, this approach works even when some steps are manual processes.