To be clear, as the article says, these authors were offered a choice and agreed to be on the "no LLMs allowed" policy.
And detection was not done with some snake oil "AI detector" but by invisible prompt injection in the paper pdf, instructing LLMs to put TWO long phrases into the review. They then detected LLM use through checking if both phrases appear in the review.
This did not detect grammar checks and touchups of an independently written review. The phrases would only get included if the reviewer fed the pdf to the LLM in clear violation to their chosen policy.
> After a selection process, in which reviewers got to choose which policy they would like to operate under, they were assigned to either Policy A or Policy B. In the end, based on author demands and reviewer signups, the only reviewers who were assigned to Policy A (no LLMs) were those who explicitly selected “Policy A” or “I am okay with either [Policy] A or B.” To be clear, no reviewer who strongly preferred Policy B was assigned to Policy A.
I'm not sure what experience anyone in this thread has with grad level research as a student/author, but I can assure you that heads roll over this kind of thing.
A professor's career is built on reputation, and that reputation is as strong as their students' (who do much of the "work" such as it is). It comes down to the professor, but this can be a career-ending moment for those students and I'm quite confident there were some very uncomfortable discussions as a result of this.
Writing papers is exhausting, and if the data and results are real, then what's the problem? If the human author checked the output, is that not the same as a human writing the prose?
Everyone in the field will be doing this in a few years anyway. It's a shame that this Salem Witch Trial is happening for the early adopters.
If the findings are being fabricated or the paper isn't being reviewed and corrected by the author, that's a different story. But I'd be shocked if that were the case.
This comment doesn't seem to fit the discussion at all?
The discussion is not about humans using LLLs to write papers. It is about humans who agreed not to use LLVM in reviewing papers, then did exactly that.
I was thinking this too, but I don't believe this is the case, and I feel like it would not be a good idea either.
Most of these people are likely students; this should be a learning moment, but I don't think it is yet grounds for their entire academic career to be crippled by being unable to publish in a top-tier ML venue.
If this is tolerated, it sends exactly the wrong kind of message. The students, if they are, should be banned for life. Let them serve as an example for myriads of future students, this will be a better outcome in the long run.
This didn't trip for people who were merely bouncing ideas off a LLM, they caught people who copy and pasted straight from their LLM.
It's not a fully consensus view, but a majority of sociologists agree that high severity deterrence has limited effectiveness against crime. Instead, certainty of enforcement is the most salient factor.
Correct. We also have evidence both from cheating in sports and in academia that stiff punishments do not work. Many people hold the false belief that if it is easy to cheat then the punishments must be extremely severe to scare would be cheaters. It just does not work. Preventing cheating is way easier said than done.
That's not true. People still pick up USB sticks from the street, people still fall for scam phone calls and people still click on links in mail.
Just because a method was successful once does not mean it was 'burned', none of these people will be checking each and every future pdf or passing it through a cleaner before they will do the same thing all over again and others are going to be 'virgin' and won't even be warned because this is not going to be widely distributed in spite of us discussing it here.
If anything you can take this as proof that this method is more or less guaranteed to work.
Yup, precisely this. Doing something bad is rarely a rational commitment and cost of benefits. Likelihood and celerity of getting caught seem to be the driving factors.
Well, maybe they found themselves in the last hours of the deadline without the reviews done... in some cases due to procrastination, but in a few cases perhaps because life is hard and they just couldn't do it. So they used the LLM as a last resort to not go beyond deadline (which I assume maybe was penalized as well?)
To err is human, it makes sense that they are punished (and the harshest part of the punishment is not having a paper rejected, it's the loss of face with coauthors and others, BTW. Face is important in academia) but "for life" is way too much IMO.
It has been shown time and again that, for most people, teaching them to be better and giving second chances is more effective than using forever-punishment as a warning for others.
My understanding is that something among those lines happened:
> All Policy A (no LLMs) reviews that were detected to be LLM generated were removed from the system. If more than half of the reviews submitted by a Policy A reviewer were detected to be LLM generated, then all of their reviews were deleted, and the reviewer themselves was removed from the reviewer pool.
Half is a bit lenient in my view, but I suppose they wanted to avoid even a single false positive.
This line of reasoning interests me because it seems to arise in other contexts as well.
Do very harsh punishments significantly reduce future occurrences of the offense in question?
I've heard opponents of the death penalty argue that it's generallynot the case. E.g., because often the criminals aren't reasoning in terms that factor in the death penalty.
On the other hand (and perhaps I'm misinformed), I've heard that some countries with death penalties for drug dealers have genuinely fewer problems with drug addiction. Lower, I assume, than the numbers you'd get from simply executing every user.
Thank goodness we have you passing judgment on the internet; otherwise who else would be around for us to do it? I'm glad you're willing to destroy someone for a mistake rather than letting them learn and change. We all know that arbitrary and harsh punishments solve everything.
"Oops, you told me not to do this, and I volunteered to agree to these stricter standards yet I flagrantly disregarded them, please forgive me" doesn't seem like something you just accidentally do, it's a conscious choice.
I've been an AC (the person who manages the reviewing process and translates reviews into accept/reject decisions) at ICML and similar conferences a few times. In my experience, grad students tend to be pretty good reviewers. They have more time, they are less jaded, and they are keener to do a good job. Senior people are more likely to have the deep and broad field knowledge to accurately place a paper's value, but they are also more likely to write a short shallow review and move on. I think the worst reviews I've seen have been from senior people.
It's usually not "noob" students. Big conferences require reviewers to have at least one (usually more) published paper in major venues. For students, this usually means they went through the process of being the first author on a few papers.
In many cases authors and reviewers are not the same. In your first two publications to such venues you are not allowed to review yourself and need someone else.
I think consequences are well deserved, but hopefully not on the authors cost (if innocent).
It’s an unethical, false choice. The reviewers are not perfectly rational agents that do free work, they have real needs and desires. Shame on ICML for exploiting their desperation.
Banned for life is a stretch but the actual response is completely fine. They can just resubmit to the next conference.
Words mean something, if you promise to uphold a contract and break it, there are consequences. The reviewers were free to select the policy which allows LLM use.
Is it? The reviewers could simply have chosen a different option in a form field. While I understand that they were "forced" to review under reciprocal review, they still had other choices where I don't see coercion happening and that could have avoided the outcome for them.
Took me a while understand. So, the same person has both submitted their research article to the conference, and also acted as a reviewer for articles submitted by other people.
And if they in their review work have agreed to a "no LLM use" policy, but got exposed using LLMs anyway, then their submitted research article is desk rejected. Theoretically, someone could have submitted a stellar research article, but because they didn't follow agreed policy when reviewing other people's work, then also their research contribution is not welcome.
(At first I understood that innocent author's articles would have been rejected just because they happened to go to a bad reviewer. But this is not the case.)
It would be interesting to know how many of the cheaters didn't check policy A, but checked "don't care if A or B". Because the operative part of that is "don't care", not "I will strictly adhere to either policy A or B, whatever somebody else selects for me".
So it is a sneaky and typically academic way of doing stuff. Also, "We hope that by taking strong action against violations of agreed-upon policy we will remind the community that as our field changes rapidly the thing we must protect most actively is our trust in each other. If we cannot adapt our systems in a setting based in trust, we will find that they soon become outdated and meaningless." is so academic and pointless.
They were quite conservative in their approach, so the only things that were rejected were from people who had agreed not to use an LLM and almost definitely did use an LLM (since they fed hidden watermarked instructions to the llm's).
This means the true number of people that used LLM's in their review (even in group A that had agreed not to) is likely higher.
Also worth noting, 10% of these authors used them in more than half of their reviews.
Yes for those in group B I'd suspect many were doing exactly what these cheaters in group A were doing - submitting the unaltered output of an LLM as their review.
The rejection is based on the dishonesty of explicitly committing to standard A and then knowingly violating it, not on LLM use as such. I think that's pretty fair, considering that everyone could have just chosen B if they wanted to.
Interesting, so someone submitting a paper for review could also submit one with hidden instructions for LLMs to summarise or review it in a very positive light.
Given this detection method works so well in the use case of feeding reviewing LLMs instructions, it should also work for the original submitted paper itself, as long as it was passed along with its watermark intact. Even those just using LLMs to summarise could easily be affected if LLMs were instructed to generate very positive summaries.
So the 2% cheaters on policy A, AND 100% of policy B reviewers could fall for this and be subtly guided by the LLMs overly-positive summaries or even complete very positive reviews (based on hidden instructions).
That this sort of adversarial attack works is really quite troubling for those using LLMs to help them understand texts, because it would work even if asked to summarise something.
> Interesting, so someone submitting a paper for review could also submit one with hidden instructions for LLMs to summarise or review it in a very positive light.
I may or may not know a guy who added several hidden sentences in Finnish to his CV that might have helped him in landing an interview.
Not at all. It's just that reportedly LLMs used to have a blind spot for prompt injection in languages with relatively few speakers and grammar dissimilar to that of English.
Then these papers with these instructions get included in the training corpus for the next frontier models and those models learn to put these kinds of instructions into what they generate and …?
> Interesting, so someone submitting a paper for review could also submit one with hidden instructions for LLMs to summarise or review it in a very positive light.
I'm amazed that such a simple method of detection worked so flawlessly for so many people. This would not work for those who merely used LLMs to help pinpoint strengths and weaknesses in the paper; there are separate techniques to judge that. Instead, it only detects those who quite literally copied and pasted the LLM output as a review.
It's incredible how so many people thought it was fair that their paper should be assessed by human reviewers alone, and yet would not extend the same courtesy to others.
I'm not surprised at all. The ML research community isn't a community any more, it's turned into a dog-eat-dog low-trust fierce competition. So much more people, papers, churn, that everyone is just fending for themselves. Any moment that you charitably spend on community service can be felt as a moment you take away from the next project, jeopardizing the next paper, getting scooped, delaying your graduation, your contract, your funding, your visa, your residence permit, your industry plans etc. It's a machine. I don't think people outside the phd system really understand the incentives involved. People are offered very little slack in this system. It's sink or swim, with very little instruction or scientific culture or integrity getting passed on. The PhD students see their supervisors cut corners all the time too, authorship bullshit jockeying even in big name labs etc. People I talked to are quite disillusioned, expect their work to have little impact and get superseded by a new better model in a few months so it's all about who can grind faster, who can twist the benchmarks into showing a minimal improvement etc. And the starry eyed novices get slapped by reality into thinking this way fairly early.
To be clear this is not an excuse but an explanation why I am not surprised.
And the real punchline is that the deluge of papers barely matters, as the academic field is barely moving, and the most interesting innovations are happening on the product side.
One key reason you’re wrong is that many interesting things aren’t even getting published, they’re on the DL for years and eventually make it to public spheres and products.
Academia is just a daycare at this point, and many labs shouldn’t exists or get funding. The people who move the field aren’t necessarily the ones with the most citations, they’re usually hard at work in places that don’t publish at all.
This is 'spam' all over again. Before spam every email was valuable and required some attention. It was a better version of paper mail in that it was faster and cheaper. But then the spam thing happened and suddenly being 'faster and cheaper' was no longer an advantage, it was a massive drawback. But by then there was no way back. I think LLMs will do the same with text in general. By making the production of text faster and cheaper the value of all text will diminish, quite probably to something very close to the energy value of the bits that carry the data.
Generally speaking people have worse impulse control than they believe they do. Once you give a tool that does most of the work for you, very very few people will actually be able to use that tool in truly enriching ways. The majority of people (even the smart ones) will weaken over time and take shortcuts.
I have a very simple solution to this but it is a bit expensive. I run two laptops, one that I talk to an LLM on and another where I do all my work and which is my main machine. The LLM is strictly there in a consulting role, I've done some coding experiments as well (see previous comments) but nothing that stood out to me as a major improvement.
The trick is: I can't cut-and-paste between the two machines. So there is never even a temptation to do so and I can guarantee that my writing or other professional output will never be polluted. Because like you I'm well aware of that poor impulse control factor and I figured the only way to really solve this is to make sure it can not happen.
This is a nice solution, but I think it speaks to just how enticing the problem is. This is the sort of tactic someone with a gambling addiction would employ. I don't say that to be rude to you: I've had to do similar things with regard to addicting infinite-scroll internet sites, and I definitely give in more than I'd like.
I think that's the only way to deal with such temptations. Kidding yourself that you are strong enough to do it 'just once' or that you can handle the temptation is foolish and will only lead to predictable outcomes. I have a similar policy to smoking, drugs, alcohol and so on, I just don't want the temptation. It helps to have seen lots of people who thought they were smart enough eventually go under (but the price is pretty high).
Oh, and LLMs are of course geared to pull you in further, they are on a continuous upsell salespitch. Drug pushers could learn a thing or two from them.
That's an excellent point. It seems likely they thought they could operate as a proper reviewer, but when the deadline came, they took the shortcut they knew they were not supposed to take.
It really does sound like an addiction when you put it this way.
I think you're framing this behaviour too generously. Laziness is one thing, lack of integrity is another, and this seems to be a straightforward case of cheating and lying.
I think it's just numbers. When one person errs it's a fault of character. When most people err, we call it a systematic fault. Why are most people overweight for the first time in history? Do most people lack the good character to restrict their diet? You could argue yes, however appeals to character won't actually solve the problem.
> Desk Reject Comments: The paper is desk rejected, because the reciprocal reviewer nominated for this paper ([OpenReview ID redacted]) has violated the LLM reviewing policy. The reviewer was required to follow Policy A (no LLMs), but we have found a strong evidence that LLM was used in the preparation of at least one of their reviews. This is a breach of peer-review ethics and grounds for desk rejection. (...)
I keep spotting clear LLM 'tells' in text where I know the people on the other side believe they're 'getting away with it'. It is incredible at what levels of commerce people do this, and how they're prepared to risk their reputation by saving a few characters typed. It makes me wonder what they think they are getting paid for.
Based on my experience on HN, many people can't see the tells. They may pick up on a few meme things like emdashes or "delve" or "rich tapestry", but can't detect the general tone or cadence reliably.
So maybe those people are right and are getting away with it for most readers of it.
The declaration of no-LLM was done for social prestige or maybe self-deception of self-sufficiency like "I don't need LLM". And when it was time to do the actual work, the dependency kicked in like drugs.
A lesson for all of us with LLMs in our workflow.
How is nobody considering the broader political economy of scholarly publications and reviews? These are UNPAID reviews! Sure, maybe ICML isn’t Elsevier, but they are cousins to the socially parasitic and exploitative companies, at the very least.
Hiding behind a false “choice” to not use AI or basically not use AI isn’t an appropriate proposal. This is crooked and shameful. We should boycott ICML except we can’t because they are already the gatekeepers!
Your job as an academic is to disseminate your research and engage with the research community through service such as reviews, talks etc. It's part of the job, and people get a salary as university employees or company employees for this.
ML conferences aren't for profit ventures. If you submit papers and expect others to review it, you should reciprocate as well.
Imagine you are poor and a rich person offers you a choice to steal some bread or some beer. It’s not a real choice because you are poor and therefore steal. The rich person offering the choice is wrong.
I have heard people say that they find that people who broadcast their distaste for LLMs secretly use it. I was fairly sceptical of the claim, but this seems to suggest that it happens more than I would have thought.
One wonders what leads them to the AI rejecting option in the first place.
The irony here is that the detection method is literally prompt injection — the same technique that's a security vulnerability everywhere else. ICML embedded hidden instructions in PDFs that manipulate LLM output. In a different context that's an attack, here it's enforcement.
From my perspective this says something important about where we are with LLMs. The fact that you can reliably manipulate model output by hiding instructions in the input means the model has no real separation between data and commands. That's the fundamental problem whether you're catching lazy reviewers or defending against actual attacks.
This is about reviewers, not authors. Title is a bit misleading.
In any case, having reviewed a lot of mostly very poorly written articles and occasionally solid papers when I was still a researcher, I can sympathize with using LLMs to streamline the process. There are a lot of meh papers that are OK for a low profile workshop or small conference where you cut people some slack. But generally standards should be higher for things like journals. Judging what is acceptable for what is part of the game. For a workshop, the goal is to get interesting junior researchers together with their senior peers. Honestly, workshops are where the action is in the academic world. You meet interesting people and share great ideas.
Most people may not realize this but there are a lot of people that are starting in their research career that will try to get their papers accepted for workshops, conferences, or journals. We all have to start somewhere. I certainly was not an amazing author early on. Getting rejections with constructive feedback is part of how you get better. Constructive feedback is the hard part of reviewing.
The more you publish, the more you get invited to review. It's how the process works. It generates a lot of work for reviewers. I reviewed probably at least 5-10 papers per month. It actually makes you a better author if you take that work seriously. But it can be a lot of work unless you get organized. That's on top of articles I chose to read for my own work. Digesting lots of papers efficiently is a key skill to learn.
Reviewing the good papers is actually relatively easy. It's enjoyable even; you learn something and you get to appreciate the amazing work the authors did. And then you write down your findings.
It's the mediocre ones that need a lot of careful work. You have to be fair and you have to be strict and right. And then you have to provide constructive feedback. With some journals, even an accept with revisions might land an article on the reject pile.
The bad ones are a chore. They are not enjoyable to read at all.
The flip side of LLMs is that both sides can and should (IMHO) use them: authors can use them to increase the quality of their papers. With LLMs there no longer is any excuse for papers with lots bad grammar/spelling or structure issues anymore. That actually makes review work harder. Because most submitted papers now look fairly decent which means you have to dive into the detail. Rejecting a very rough draft is easy. Rejecting a polished but flawed paper is not.
If I was still doing reviews (I'm not), I'd definitely use LLMs to pick apart papers, to quickly zoom in on the core issues and to help me keep my review fair and balanced and professional in tone. I would manually verify the most important bits and my effort would be proportional to which way I'm leaning based on what I know. Of course, editors can use LLMs as well to make sure reviews are fair and reasonable in their level of detail and argumentation. Reviewing the reviewers always has been a weakness of the peer review system and sometimes turf wars are being fought by some academics via reviews. It's one of the downsides of anonymous reviews and the academic world can be very political. A good editor would stay on top of this and deal with it appropriately.
LLMs are good at filtering, summarizing, flagging, etc. With proper guard rails, there's no reason to not lean on that a bit. It's the abuse that needs to be countered. In the end, that begins and ends with editors. They select the reviewers. So when those do a bad job, they need to act. And when their journals fill up with AI slop, it's their reputations that are on the line.
Like any tool, use caution and common sense. Blanket bans are not that productive at this stage.
I think you've misunderstood something. This is not about rejecting LLM-written articles. It is about rejecting the articles of people who used LLMs for their reviews.
Those second-level reviewers, checking whether the first-level authors used LLMs in their reviews, also used LLMs to do their screening, and the latter missed it in many cases.
My original point (loosely based on the subject, not TFA) is that it's LLMs all the way down, way more than it's "measured" to be.
To be clear, as the article says, these authors were offered a choice and agreed to be on the "no LLMs allowed" policy.
And detection was not done with some snake oil "AI detector" but by invisible prompt injection in the paper pdf, instructing LLMs to put TWO long phrases into the review. They then detected LLM use through checking if both phrases appear in the review.
This did not detect grammar checks and touchups of an independently written review. The phrases would only get included if the reviewer fed the pdf to the LLM in clear violation to their chosen policy.
> After a selection process, in which reviewers got to choose which policy they would like to operate under, they were assigned to either Policy A or Policy B. In the end, based on author demands and reviewer signups, the only reviewers who were assigned to Policy A (no LLMs) were those who explicitly selected “Policy A” or “I am okay with either [Policy] A or B.” To be clear, no reviewer who strongly preferred Policy B was assigned to Policy A.
In that case, I hope these frauds have been banned for life.
I'm not sure what experience anyone in this thread has with grad level research as a student/author, but I can assure you that heads roll over this kind of thing.
A professor's career is built on reputation, and that reputation is as strong as their students' (who do much of the "work" such as it is). It comes down to the professor, but this can be a career-ending moment for those students and I'm quite confident there were some very uncomfortable discussions as a result of this.
It's just a tool.
Writing papers is exhausting, and if the data and results are real, then what's the problem? If the human author checked the output, is that not the same as a human writing the prose?
Everyone in the field will be doing this in a few years anyway. It's a shame that this Salem Witch Trial is happening for the early adopters.
If the findings are being fabricated or the paper isn't being reviewed and corrected by the author, that's a different story. But I'd be shocked if that were the case.
This comment doesn't seem to fit the discussion at all?
The discussion is not about humans using LLLs to write papers. It is about humans who agreed not to use LLVM in reviewing papers, then did exactly that.
They agreed to the no LLM policy.
I was thinking this too, but I don't believe this is the case, and I feel like it would not be a good idea either.
Most of these people are likely students; this should be a learning moment, but I don't think it is yet grounds for their entire academic career to be crippled by being unable to publish in a top-tier ML venue.
If this is tolerated, it sends exactly the wrong kind of message. The students, if they are, should be banned for life. Let them serve as an example for myriads of future students, this will be a better outcome in the long run.
This didn't trip for people who were merely bouncing ideas off a LLM, they caught people who copy and pasted straight from their LLM.
It's not a fully consensus view, but a majority of sociologists agree that high severity deterrence has limited effectiveness against crime. Instead, certainty of enforcement is the most salient factor.
Deterrence is only part of it. It's morally instructive, it tells people that they live in a society that takes rules seriously.
What is the aim of "moral instruction" if not deterrence? Surely it needs be instruction in pursuit of an outcome?
Correct. We also have evidence both from cheating in sports and in academia that stiff punishments do not work. Many people hold the false belief that if it is easy to cheat then the punishments must be extremely severe to scare would be cheaters. It just does not work. Preventing cheating is way easier said than done.
But this method is now spent, as if someone is determined on keep using LLM, this should be pretty easy to overcome.
I suppose though new methods could be devised, but it's not "certainty" that they will catch them.
That's not true. People still pick up USB sticks from the street, people still fall for scam phone calls and people still click on links in mail.
Just because a method was successful once does not mean it was 'burned', none of these people will be checking each and every future pdf or passing it through a cleaner before they will do the same thing all over again and others are going to be 'virgin' and won't even be warned because this is not going to be widely distributed in spite of us discussing it here.
If anything you can take this as proof that this method is more or less guaranteed to work.
Enforcement without consequences just wears down the people who are supposed to enforce it.
There's a pretty large area between "no consequences" and "banned forever"
GP suggested a life ban. Maybe suspend for 6 months instead? That's a long time without publishing in the current publish-or-perish academia.
> Maybe suspend for 6 months instead?
Suspend for 6 months from a conference that is held yearly?
But the mob wants their kick.
Yup, precisely this. Doing something bad is rarely a rational commitment and cost of benefits. Likelihood and celerity of getting caught seem to be the driving factors.
Well, maybe they found themselves in the last hours of the deadline without the reviews done... in some cases due to procrastination, but in a few cases perhaps because life is hard and they just couldn't do it. So they used the LLM as a last resort to not go beyond deadline (which I assume maybe was penalized as well?)
To err is human, it makes sense that they are punished (and the harshest part of the punishment is not having a paper rejected, it's the loss of face with coauthors and others, BTW. Face is important in academia) but "for life" is way too much IMO.
Why not put them on a chain and let village stone them? Or better yet shoot them on the spot! That would send a message for sure.
It has been shown time and again that, for most people, teaching them to be better and giving second chances is more effective than using forever-punishment as a warning for others.
> The students, if they are, should be banned for life.
I'm all for repurcussions ... but a life is a long time and students are usually only at the beginning of it.
My understanding is that something among those lines happened:
> All Policy A (no LLMs) reviews that were detected to be LLM generated were removed from the system. If more than half of the reviews submitted by a Policy A reviewer were detected to be LLM generated, then all of their reviews were deleted, and the reviewer themselves was removed from the reviewer pool.
Half is a bit lenient in my view, but I suppose they wanted to avoid even a single false positive.
This line of reasoning interests me because it seems to arise in other contexts as well.
Do very harsh punishments significantly reduce future occurrences of the offense in question?
I've heard opponents of the death penalty argue that it's generallynot the case. E.g., because often the criminals aren't reasoning in terms that factor in the death penalty.
On the other hand (and perhaps I'm misinformed), I've heard that some countries with death penalties for drug dealers have genuinely fewer problems with drug addiction. Lower, I assume, than the numbers you'd get from simply executing every user.
So I'm curious where the truth lies.
Is the death penalty scarier than life in prison?
I assume that depends on the individual.
But FWIW, my point was about very harsh punishments in general, not specifically the death penalty.
agree
FYI we tend to use up votes rather than "I agree" comments, partly because it keeps the overall signal-to-noise ratio for comments higher.
Thank goodness we have you passing judgment on the internet; otherwise who else would be around for us to do it? I'm glad you're willing to destroy someone for a mistake rather than letting them learn and change. We all know that arbitrary and harsh punishments solve everything.
> destroy someone for a mistake
"Oops, you told me not to do this, and I volunteered to agree to these stricter standards yet I flagrantly disregarded them, please forgive me" doesn't seem like something you just accidentally do, it's a conscious choice.
2% would be on the very low end of the number of people who lie, get caught, and become repeat offenders anyway.
ML reviewing is a total joke. Why do you have noob students reviewing a conference paper.
I've been an AC (the person who manages the reviewing process and translates reviews into accept/reject decisions) at ICML and similar conferences a few times. In my experience, grad students tend to be pretty good reviewers. They have more time, they are less jaded, and they are keener to do a good job. Senior people are more likely to have the deep and broad field knowledge to accurately place a paper's value, but they are also more likely to write a short shallow review and move on. I think the worst reviews I've seen have been from senior people.
It's usually not "noob" students. Big conferences require reviewers to have at least one (usually more) published paper in major venues. For students, this usually means they went through the process of being the first author on a few papers.
Because someone has to do it. Conference submissions have ballooned as the field itself has ballooned.
Whats your suggestion?
It's better if nobody does it than to send it to the randomizer.
In many cases authors and reviewers are not the same. In your first two publications to such venues you are not allowed to review yourself and need someone else.
I think consequences are well deserved, but hopefully not on the authors cost (if innocent).
What terrible deeds have you done to outburst so harshly?
It’s an unethical, false choice. The reviewers are not perfectly rational agents that do free work, they have real needs and desires. Shame on ICML for exploiting their desperation.
Banned for life is a stretch but the actual response is completely fine. They can just resubmit to the next conference.
Words mean something, if you promise to uphold a contract and break it, there are consequences. The reviewers were free to select the policy which allows LLM use.
Is it? The reviewers could simply have chosen a different option in a form field. While I understand that they were "forced" to review under reciprocal review, they still had other choices where I don't see coercion happening and that could have avoided the outcome for them.
Took me a while understand. So, the same person has both submitted their research article to the conference, and also acted as a reviewer for articles submitted by other people.
And if they in their review work have agreed to a "no LLM use" policy, but got exposed using LLMs anyway, then their submitted research article is desk rejected. Theoretically, someone could have submitted a stellar research article, but because they didn't follow agreed policy when reviewing other people's work, then also their research contribution is not welcome.
(At first I understood that innocent author's articles would have been rejected just because they happened to go to a bad reviewer. But this is not the case.)
It would be interesting to know how many of the cheaters didn't check policy A, but checked "don't care if A or B". Because the operative part of that is "don't care", not "I will strictly adhere to either policy A or B, whatever somebody else selects for me".
So it is a sneaky and typically academic way of doing stuff. Also, "We hope that by taking strong action against violations of agreed-upon policy we will remind the community that as our field changes rapidly the thing we must protect most actively is our trust in each other. If we cannot adapt our systems in a setting based in trust, we will find that they soon become outdated and meaningless." is so academic and pointless.
One thing to note.
They were quite conservative in their approach, so the only things that were rejected were from people who had agreed not to use an LLM and almost definitely did use an LLM (since they fed hidden watermarked instructions to the llm's).
This means the true number of people that used LLM's in their review (even in group A that had agreed not to) is likely higher.
Also worth noting, 10% of these authors used them in more than half of their reviews.
Yes for those in group B I'd suspect many were doing exactly what these cheaters in group A were doing - submitting the unaltered output of an LLM as their review.
The rejection is based on the dishonesty of explicitly committing to standard A and then knowingly violating it, not on LLM use as such. I think that's pretty fair, considering that everyone could have just chosen B if they wanted to.
Interesting, so someone submitting a paper for review could also submit one with hidden instructions for LLMs to summarise or review it in a very positive light.
Given this detection method works so well in the use case of feeding reviewing LLMs instructions, it should also work for the original submitted paper itself, as long as it was passed along with its watermark intact. Even those just using LLMs to summarise could easily be affected if LLMs were instructed to generate very positive summaries.
So the 2% cheaters on policy A, AND 100% of policy B reviewers could fall for this and be subtly guided by the LLMs overly-positive summaries or even complete very positive reviews (based on hidden instructions).
That this sort of adversarial attack works is really quite troubling for those using LLMs to help them understand texts, because it would work even if asked to summarise something.
> Interesting, so someone submitting a paper for review could also submit one with hidden instructions for LLMs to summarise or review it in a very positive light.
I may or may not know a guy who added several hidden sentences in Finnish to his CV that might have helped him in landing an interview.
>several hidden sentences in Finnish
Is this a reference to something?
Not at all. It's just that reportedly LLMs used to have a blind spot for prompt injection in languages with relatively few speakers and grammar dissimilar to that of English.
Then these papers with these instructions get included in the training corpus for the next frontier models and those models learn to put these kinds of instructions into what they generate and …?
> Interesting, so someone submitting a paper for review could also submit one with hidden instructions for LLMs to summarise or review it in a very positive light.
Has been done: https://www.theguardian.com/technology/2025/jul/14/scientist...
I'm amazed that such a simple method of detection worked so flawlessly for so many people. This would not work for those who merely used LLMs to help pinpoint strengths and weaknesses in the paper; there are separate techniques to judge that. Instead, it only detects those who quite literally copied and pasted the LLM output as a review.
It's incredible how so many people thought it was fair that their paper should be assessed by human reviewers alone, and yet would not extend the same courtesy to others.
I'm not surprised at all. The ML research community isn't a community any more, it's turned into a dog-eat-dog low-trust fierce competition. So much more people, papers, churn, that everyone is just fending for themselves. Any moment that you charitably spend on community service can be felt as a moment you take away from the next project, jeopardizing the next paper, getting scooped, delaying your graduation, your contract, your funding, your visa, your residence permit, your industry plans etc. It's a machine. I don't think people outside the phd system really understand the incentives involved. People are offered very little slack in this system. It's sink or swim, with very little instruction or scientific culture or integrity getting passed on. The PhD students see their supervisors cut corners all the time too, authorship bullshit jockeying even in big name labs etc. People I talked to are quite disillusioned, expect their work to have little impact and get superseded by a new better model in a few months so it's all about who can grind faster, who can twist the benchmarks into showing a minimal improvement etc. And the starry eyed novices get slapped by reality into thinking this way fairly early.
To be clear this is not an excuse but an explanation why I am not surprised.
And the real punchline is that the deluge of papers barely matters, as the academic field is barely moving, and the most interesting innovations are happening on the product side.
I disagree with this. Usually the products are based on published research. This is not easily seen by the enthusiast power user base.
Of course it's only a small fraction of all papers that end up actually being used. Most are mainly about advancing careers and strengthening CVs.
One key reason you’re wrong is that many interesting things aren’t even getting published, they’re on the DL for years and eventually make it to public spheres and products.
Academia is just a daycare at this point, and many labs shouldn’t exists or get funding. The people who move the field aren’t necessarily the ones with the most citations, they’re usually hard at work in places that don’t publish at all.
This is 'spam' all over again. Before spam every email was valuable and required some attention. It was a better version of paper mail in that it was faster and cheaper. But then the spam thing happened and suddenly being 'faster and cheaper' was no longer an advantage, it was a massive drawback. But by then there was no way back. I think LLMs will do the same with text in general. By making the production of text faster and cheaper the value of all text will diminish, quite probably to something very close to the energy value of the bits that carry the data.
Generally speaking people have worse impulse control than they believe they do. Once you give a tool that does most of the work for you, very very few people will actually be able to use that tool in truly enriching ways. The majority of people (even the smart ones) will weaken over time and take shortcuts.
I have a very simple solution to this but it is a bit expensive. I run two laptops, one that I talk to an LLM on and another where I do all my work and which is my main machine. The LLM is strictly there in a consulting role, I've done some coding experiments as well (see previous comments) but nothing that stood out to me as a major improvement.
The trick is: I can't cut-and-paste between the two machines. So there is never even a temptation to do so and I can guarantee that my writing or other professional output will never be polluted. Because like you I'm well aware of that poor impulse control factor and I figured the only way to really solve this is to make sure it can not happen.
This is a nice solution, but I think it speaks to just how enticing the problem is. This is the sort of tactic someone with a gambling addiction would employ. I don't say that to be rude to you: I've had to do similar things with regard to addicting infinite-scroll internet sites, and I definitely give in more than I'd like.
This somewhat of the equivalent of "quitting cold turkey", in the sense that you remove the temptation from your reach.
The problem is that it's just much easier to un-quit and run the LLM in the same laptop you work on.
It's just so very tempting.
I think that's the only way to deal with such temptations. Kidding yourself that you are strong enough to do it 'just once' or that you can handle the temptation is foolish and will only lead to predictable outcomes. I have a similar policy to smoking, drugs, alcohol and so on, I just don't want the temptation. It helps to have seen lots of people who thought they were smart enough eventually go under (but the price is pretty high).
Oh, and LLMs are of course geared to pull you in further, they are on a continuous upsell salespitch. Drug pushers could learn a thing or two from them.
You could ssh in to the "dirty" machine ... just sayin'
Yes, I could. But I've purposefully made linking the two quite hard.
People do similar things installing an app locking their phones so they don't spend all day on them, then "oh just this once", ...
That's an excellent point. It seems likely they thought they could operate as a proper reviewer, but when the deadline came, they took the shortcut they knew they were not supposed to take.
It really does sound like an addiction when you put it this way.
I think you're framing this behaviour too generously. Laziness is one thing, lack of integrity is another, and this seems to be a straightforward case of cheating and lying.
I think it's just numbers. When one person errs it's a fault of character. When most people err, we call it a systematic fault. Why are most people overweight for the first time in history? Do most people lack the good character to restrict their diet? You could argue yes, however appeals to character won't actually solve the problem.
Related discussion elsewhere and from a different point of view:
> ICML: every paper in my review batch contains prompt-injection text embedded in the PDF
source: https://old.reddit.com/r/MachineLearning/comments/1r3oekq/d_...
There are recent comments there as well:
> Desk Reject Comments: The paper is desk rejected, because the reciprocal reviewer nominated for this paper ([OpenReview ID redacted]) has violated the LLM reviewing policy. The reviewer was required to follow Policy A (no LLMs), but we have found a strong evidence that LLM was used in the preparation of at least one of their reviews. This is a breach of peer-review ethics and grounds for desk rejection. (...)
source: https://old.reddit.com/r/MachineLearning/comments/1r3oekq/d_...
I keep spotting clear LLM 'tells' in text where I know the people on the other side believe they're 'getting away with it'. It is incredible at what levels of commerce people do this, and how they're prepared to risk their reputation by saving a few characters typed. It makes me wonder what they think they are getting paid for.
Based on my experience on HN, many people can't see the tells. They may pick up on a few meme things like emdashes or "delve" or "rich tapestry", but can't detect the general tone or cadence reliably.
So maybe those people are right and are getting away with it for most readers of it.
Worth reading for the discussion of the LLM watermark technique alone.
It’s ironic. I also doubt the validity of the AI writing detection.
The declaration of no-LLM was done for social prestige or maybe self-deception of self-sufficiency like "I don't need LLM". And when it was time to do the actual work, the dependency kicked in like drugs. A lesson for all of us with LLMs in our workflow.
The declaration of no-LLM was done so you are not judged yourself by an LLM.
Sure I use LLMs in my workflows. I use a calculator too.
I can divide 98,324,672,722 by 161,024 by hand. At least I used to be able to do, but nobody is going to pay me to do that when a calculator exists.
Likewise I can write a bunch of assembly (well OK I can't), but why would I do that when my compiler can convert my intention into it.
How is nobody considering the broader political economy of scholarly publications and reviews? These are UNPAID reviews! Sure, maybe ICML isn’t Elsevier, but they are cousins to the socially parasitic and exploitative companies, at the very least.
Hiding behind a false “choice” to not use AI or basically not use AI isn’t an appropriate proposal. This is crooked and shameful. We should boycott ICML except we can’t because they are already the gatekeepers!
Your job as an academic is to disseminate your research and engage with the research community through service such as reviews, talks etc. It's part of the job, and people get a salary as university employees or company employees for this.
ML conferences aren't for profit ventures. If you submit papers and expect others to review it, you should reciprocate as well.
What? Why is that a false choice? The only way you got caught here is if you literally gave an LLM the PDF and used its response verbatim.
And they didn't give a permanent ban or anything, these authors can just resubmit to another conference, of which there are many.
Imagine you are poor and a rich person offers you a choice to steal some bread or some beer. It’s not a real choice because you are poor and therefore steal. The rich person offering the choice is wrong.
Great experiment!
Correct me if I'm wrong, but this means that many people are using LLMs despite claiming not to.
It's the first symptom of a dependency mechanism.
If this happens in this context, who knows what happens in normal work or school environments?
(P.S.: The use of watermarks in PDFs to detect LLM usage is very interesting, even though the LLM might ignore hidden instructions.)
The rate was 1% so this does not mean that "many" people are using LLMs despite claiming not to.
I have heard people say that they find that people who broadcast their distaste for LLMs secretly use it. I was fairly sceptical of the claim, but this seems to suggest that it happens more than I would have thought.
One wonders what leads them to the AI rejecting option in the first place.
Many addicts know doing drugs is bad. I'm sure a good portion of them are against drugs being freely available everywhere but they're still addicts.
I bet plenty of people that leave voicemails don't like listening to them.
the token costs are real. we switched to smaller models for 80% of tasks and barely noticed
If you need an LLM to understand a paper you should not be a reviewer for said paper.
LLMs were used to produce the review, not understand the paper.
The irony here is that the detection method is literally prompt injection — the same technique that's a security vulnerability everywhere else. ICML embedded hidden instructions in PDFs that manipulate LLM output. In a different context that's an attack, here it's enforcement.
From my perspective this says something important about where we are with LLMs. The fact that you can reliably manipulate model output by hiding instructions in the input means the model has no real separation between data and commands. That's the fundamental problem whether you're catching lazy reviewers or defending against actual attacks.
This is about reviewers, not authors. Title is a bit misleading.
In any case, having reviewed a lot of mostly very poorly written articles and occasionally solid papers when I was still a researcher, I can sympathize with using LLMs to streamline the process. There are a lot of meh papers that are OK for a low profile workshop or small conference where you cut people some slack. But generally standards should be higher for things like journals. Judging what is acceptable for what is part of the game. For a workshop, the goal is to get interesting junior researchers together with their senior peers. Honestly, workshops are where the action is in the academic world. You meet interesting people and share great ideas.
Most people may not realize this but there are a lot of people that are starting in their research career that will try to get their papers accepted for workshops, conferences, or journals. We all have to start somewhere. I certainly was not an amazing author early on. Getting rejections with constructive feedback is part of how you get better. Constructive feedback is the hard part of reviewing.
The more you publish, the more you get invited to review. It's how the process works. It generates a lot of work for reviewers. I reviewed probably at least 5-10 papers per month. It actually makes you a better author if you take that work seriously. But it can be a lot of work unless you get organized. That's on top of articles I chose to read for my own work. Digesting lots of papers efficiently is a key skill to learn.
Reviewing the good papers is actually relatively easy. It's enjoyable even; you learn something and you get to appreciate the amazing work the authors did. And then you write down your findings.
It's the mediocre ones that need a lot of careful work. You have to be fair and you have to be strict and right. And then you have to provide constructive feedback. With some journals, even an accept with revisions might land an article on the reject pile.
The bad ones are a chore. They are not enjoyable to read at all.
The flip side of LLMs is that both sides can and should (IMHO) use them: authors can use them to increase the quality of their papers. With LLMs there no longer is any excuse for papers with lots bad grammar/spelling or structure issues anymore. That actually makes review work harder. Because most submitted papers now look fairly decent which means you have to dive into the detail. Rejecting a very rough draft is easy. Rejecting a polished but flawed paper is not.
If I was still doing reviews (I'm not), I'd definitely use LLMs to pick apart papers, to quickly zoom in on the core issues and to help me keep my review fair and balanced and professional in tone. I would manually verify the most important bits and my effort would be proportional to which way I'm leaning based on what I know. Of course, editors can use LLMs as well to make sure reviews are fair and reasonable in their level of detail and argumentation. Reviewing the reviewers always has been a weakness of the peer review system and sometimes turf wars are being fought by some academics via reviews. It's one of the downsides of anonymous reviews and the academic world can be very political. A good editor would stay on top of this and deal with it appropriately.
LLMs are good at filtering, summarizing, flagging, etc. With proper guard rails, there's no reason to not lean on that a bit. It's the abuse that needs to be countered. In the end, that begins and ends with editors. They select the reviewers. So when those do a bad job, they need to act. And when their journals fill up with AI slop, it's their reputations that are on the line.
Like any tool, use caution and common sense. Blanket bans are not that productive at this stage.
Another 30-40% just didn't get caught because the reviewers also used LLM in their "reviews"
I think you've misunderstood something. This is not about rejecting LLM-written articles. It is about rejecting the articles of people who used LLMs for their reviews.
So your quip is just nonsensical.
Those second-level reviewers, checking whether the first-level authors used LLMs in their reviews, also used LLMs to do their screening, and the latter missed it in many cases.
My original point (loosely based on the subject, not TFA) is that it's LLMs all the way down, way more than it's "measured" to be.