There’s sloppy science, and there’s AI slop science.
In an ironic twist of fate, beleaguered AI researchers are warning that the field is being choked by a deluge of shoddy academic papers written with large language models, making it harder than ever for high quality work to be discovered and stand out.
Part of the problem is that AI research has surged in popularity. The more people who jump on the wagon, the more some are trying to speedrun an academic reputation by churning out dozens — and sometimes even hundreds — of papers a year, giving the entire pursuit a bad name.
In an interview with The Guardian, professor of computer science at UC Berkeley Hany Farid called the state of affairs a “frenzy.” With so much slop rising to the top, he says he now advises his students not to enter the field.
“So many young people want to get into AI,” Farid told The Guardian. “It’s just a mess. You can’t keep up, you can’t publish, you can’t do good work, you can’t be thoughtful.”
Farid stirred debate over the topic by calling out the output of an AI researcher named Kevin Zhu, who claims to have published 113 papers on AI this year.
“I can’t carefully read 100 technical papers a year,” Farid wrote in a LinkedIn post last month, “so imagine my surprise when I learned about one author who claims to have participated in the research and writing of over 100 technical papers in a year.”
Zhu, who recently received his bachelor’s in computer science at UC Berkeley — the same place that Farid teaches —launched an AI researcher program aimed at high schoolers and college students called Algoverse. Many of its participants are coauthors on Zhu’s papers, The Guardian noted. Each student pays $3,325 for a 12-week online course, during which they’re expected to submit work to AI conferences.
One of those conferences is NeurIPS, which is considered to be one of the big three conferences in a field that was once obscure but is now the center of attention as AI commands immense investment and social cachet. In 2020 it fielded less than 10,000 papers, according to The Guardian. This year, that number has jumped to over 21,500, a trend shared by other major AI conferences. The explosion has been so extreme that NeurIPS is now relying on PhD students to help review its flood of submissions.
The overwhelming volume is thanks to people like Zhu: 89 of his over a century of papers are being presented at NeurIPS this week.
Farid called Zhu’s papers a “disaster,” and added that he “could not have possibly meaningfully contributed” to them.
“I’m fairly convinced that the whole thing, top to bottom, is just vibe coding,” Farid said using the new slang that’s emerged to describe using AI tools to quickly build software, exemplifying the attitude of reckless abandon that the new crop of AI-dependent programmers are taking to the practice. Zhu would not confirm or deny whether his papers were written with AI when asked by The Guardian, but said his teams used “standard productivity tools such as reference managers, spellcheck, and sometimes language models for copy-editing or improving clarity.”
The role that AI has rapidly carved out in academic research has been a point of controversy ever since it first surged in popularity several years ago. Tools like ChatGPT are still prone to hallucinating citations, or inventing sources that do not exist, which often sneak through the peer review process of even prestigious journals. Other instances, such as when a peer-reviewed paper used an AI-generated diagram of a mouse with impossibly super-sized genitalia, make you question if there’s any oversight at all. The tech is so entrenched in academia that some clever authors are inserting hidden text into their papers designed to trick “reviewers” that are themselves AI-powered into giving positive assessments of their work.
What’s particularly disconcerting to hear now, however, is how AI research is beginning to be torn apart by the technology itself. How long can the pursuit survive its own product? And what does that mean for the upcoming generation of AI scientists, if novel research is being drowned out by their far more prolific peers that are churning out studies with fabricated sources?
Even a seasoned vet like Farid says it’s now makes it impossible to keep track of what’s happening in the AI field.
“You have no chance, no chance as an average reader to try to understand what is going on in the scientific literature,” Farid told The Guardian. “Your signal-to-noise ratio is basically one. I can barely go to these conferences and figure out what the hell is going on.”
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