My problem therefore is: we are seeing more and more papers written with tools that are known to make up facts, citations, and even entire papers. And the number of papers has increased, too. I therefore see it less as "people are being more productive" and more "people are releasing bad science much faster than we can keep up with".
Cars have plenty of advantages, and yet no one would say "the number of pedestrians killed by cars is rising, but that's an issue with the drivers". In fact, the opposite is true: from fines and school zones to speed bumps and bollards, we have accepted that cars bring structural problems with them that cannot be solved at the driver level alone.
> we need new tools to filter out junk
Agreed, but if my office suddenly was flooded with garbage my first thought wouldn't be "I need more, bigger trash cans" but rather "who brought all this junk here and why?". To simply assume that the garbage is a sudden natural phenomena that I have to live with seems, at the very least, unfair.
It's a signal:noise ratio thing. If 1 out of every 1000 AI-written papers are bad, it makes sense to put in a filter that auto-rejects any paper that has AI tells.
After all, if that 1 researcher was any good, he wouldn't have used AI to write the thing in the first place.
Publishing was always about getting past the filters. There's one more filter - "AI-generated content" - so do what you have to to get past it. IOW, write your own paper.
I am almost certain he was "hallucinating" the results. This was in the 2010s
There are well known issues in academic publishing, though I imagine it has become much noisier like open source
A possible conclusion for this could be: If the majority of CS papers is AI written, let's just accept this reality universally and stop worrying about it altogether.
I see plenty of anecdotal evidence that models have been trained fantastically well—and getting better—at writing to trigger the right neurons in the human population to produce “This is interesting/informative/correct” responses in bulk.
Could their ability to produce those responses run far ahead of their ability to actually achieve the last in reality? Sure seems plausible, and then where are we?
My anecdotal evidence is the LLM-generated, inchoate technical dross that is routinely upvoted onto the hn front page. Much of it isn’t even coherent enough to be wrong, but the readership here finds it interesting!
That's a big "If".
If a research is good, the author still has to clear all the hurdles in publishing. "Writing your own paper" is just one more hurdle.
> A possible conclusion for this could be: If the majority of CS papers is AI written, let's just accept this reality universally and stop worrying about it altogether.
That's just a different way of saying "if the majority of CS papers are crap, lets just accept this reality".
So, go on, publish away all your AI-induced "research", but the bar is slowly going to be raised anyway to reject that. That's how science always worked - when a bar is not sufficient to exclude the crap, it is raised.
Reviewers were also totally unengaged. Of 20 reviews I read (from my reviewers or from reviewers on the same papers), maybe 2 were mediocre, and the rest were crap (though likely not AI).
The notion that science will somehow benefit from this is about as stupid an idea as you can have. Science relies on skepticism. AIs are not skeptical, and many folks are submitting papers because they stand to gain something, not because they are motivated to do good research or develop new understanding. Fields are being inundated with garbage that is maximally indistinguishable from real work (that's the training objective for LLMs). This in turn maximizes the cost of identifying bad work.
This is the same enshittification process that we see everywhere else. You get spam phone calls because there is no reason for a spammer not to call you. "Researchers" are submitting spam papers because there is no cost to doing so with some possible gain. Absent intervention, this eventually drives the community value of the network to zero (or potentially negative, if friction costs to switching are high).
Other problems include: Signal to Noise Ratio going through the roof.
Yes, I feel like there's room to improve things, I just strongly doubt that "using AI to detect AI" is a particularly useful thing to do here.
That’s a big if. We all know that’s not what’s happening.
That's a big if. ArXiv is not peer reviewed and LLMs basically interpolate and extrapolate text, which makes them essentially fluff generators. Even in the most charitable interpretation, LLMs enable those with nothing to say to say nothing while meeting surface-level style guides.
Why don't you read them and see? The ones I looked at were clear slop.
That's a huge assumption, and one that goes against the whole notion of using LLMs to generate text. AI slop is by far the norm.
If fake papers weren't already a big problem before AI and the fields had already been policing themselves adequately, if this was already a functioning high-trust domain, maybe we could ignore this a bit more, but the fields already manifestly had problems. People taking advantage of that are reasonably more likely to use AI. The pressures to publish or perish provide the voltage and the AIs are a rather convenient path-to-ground.
I agree in some sense that if a truth is published, it doesn't matter if the AI or a human published it. However there are perfectly reasonable reasons to be concerned that AI usage is correlated to not publishing truths, especially in a world where merely being human-generated was already not an adequate check against that.
Pre-LLMs, a paper with no spelling or grammar errors showed that somebody had put effort into writing and editing it. If they cared about the presentation, they probably also cared about the content. LLMs routinely produce nonsense that looks superficially like high-quality work.
There are far too many papers to read all of them. LLM slop is evidence that something is probably low quality. As the saying goes, "if you can't be bothered writing it, I can't be bothered reading." The rare outliers will get enough citations and recommendations to overcome this filter.
if 50% of the work is nonsense, then there's a serious concern that we can't move forward at all.
What about the papers that graduate to proper publication?
Arxiv is full of pre-prints that anyone can upload.
You now (at least for some categories) have to receive endorsement from someone who has multiple recent papers on arxiv in the same (or adjacent) category.
Perhaps the most darkly amusing consequence of this particular mania is that by poisoning the majority of our information environment with hallucinated slop, we have likely crippled the next several generations of machine-learning techniques before they're even invented! Small, locally-hostable LLMs will rattle along spewing spam long after the broader "genai bubble" pops, and building clean training datasets will permanently be more difficult and expensive.
AI hallucinates and makes up stuff 100% percent of the time. Never been a fan of that word for this.
Again, I fail to see the problem here that isn't solved by careful reading WHICH IS WHAT PEOPLE SHOULD BE DOING ANYWAY. I would like to see room for AI disclosure, maybe a statement of "this is how much AI I used."
But this blanket X% of this looks like AI? Again, so what?
Trust is very important to human progress.
In other words, it "sounds smart" without necessarily having anything to back it up. In even more critical terms, it's very good at bullshitting.
Unfortunately for us, the scientific community current relies on a certain amount of trust. (To do otherwise is very expensive! see: bitcoin). When you introduce a known-bullshitter to write your papers, every human in the loop effectively has to defend against an adversarial attack. Not just the readers at home, or the peer reviewers, but even the author needs to be wary that the facts and arguments coming out of the LLM are true and meaningful.
Personally, I've been a minor contributor to several high-profile papers. I don't know how every field does it, but in my experience, the corresponding author (generally the PI or other senior scientist), is responsible for the accuracy of the paper. They ultimately have to trust the people who did the work that the facts are true. Introducing LLMs into the mix make it more difficult for them to identify and review sections they are unsure of. (An honest person will typically write at a confidence level reflecting their certainty. LLMs do not do this in any reliable way.)
I've also found that LLMs frequently use metaphors that are unhelpful, or used out of context in a field that isn't familiar with them. This makes understanding the text more work, for no good reason. Introducing terms or definitions with low relevance reads as impressive at first glance, but avoiding the standard terminology in the field just adds confusion. (As an analogy, imagine if you were reading a CS paper that, for no particular reason, devoted a section to a new data structure called an "akimbo tree," which after much untangling, you realized was a reinvention of a randomized splay tree.)