[neilv]

> If these are the only errors, we are not troubled. However: we do not know if these are the only errors, they are merely a signature that the paper was submitted without being thoroughly checked for hallucinations. They are a signature that some LLM was used to generate parts of the paper and the responsible authors used this LLM without care.

I am troubled by people using an LLM at all to write academic research papers.

It's a shoddy, irresponsible way to work. And also plagiarism, when you claim authorship of it.

I'd see a failure of the 'author' to catch hallucinations, to be more like a failure to hide evidence of misconduct.

If academic venues are saying that using an LLM to write your papers is OK ("so long as you look it over for hallucinations"?), then those academic venues deserve every bit of operational pain and damaged reputation that will result.

[derefr]

I would argue that an LLM is a perfectly sensible tool for structure-preserving machine translation from another language to English. (Where by "another language", you could also also substitute "very poor/non-fluent English." Though IMHO that's a bit silly, even though it's possible; there's little sense in writing in a language you only half know, when you'd get a less-lossy result from just writing in your native tongue, and then having it translate from that.)

Google Translate et al were never good enough at this task to actually allow people to use the results for anything professional. Previous tools were limited to getting a rough gloss of what words in another language mean.

But LLMs can be used in this way, and are being used in this way; and this is increasingly allowing non-English-fluent academics to publish papers in English-language journals (thus engaging with the English-language academic community), where previously those academics they may have felt "stuck" publishing in what few journals exist for their discipline in their own language.

Would you call the use of LLMs for translation "shoddy" or "irresponsible"? To me, it'd be no more and no less "shoddy" or "irresponsible" than it would be to hire a freelance human translator to translate the paper for you. (In fact, the human translator might be a worse idea, as LLMs are more likely to understand how to translate the specific academic jargon of your discipline than a randomly-selected human translator would be.)

[gus_massa]

Autotranslating technical texts is very hard. After the translation, you muct check that all the technical words were translated correctly, instead of a fancy synonym that does not make sense.

(A friend has an old book translated a long time ago (by a human) from Russian to Spanish. Instead of "complex numbers", the book calls them "complicated numbers". :) )

[QuercusMax]

I remember one time when I had written a bunch of user facing text for an imaging app and was reviewing our French translation. I don't speak French but I was pretty sure "plane" (as in geometry) shouldn't be translated as "avion". And this was human translated!

[jfim]

You'd be surprised how shoddy human translations can be, and it's not necessarily because of the translators themselves.

Typically what happens is that translators are given an Excel sheet with the original text in a column, and the translated text must be put into the next column. Because there's no context, it's not necessarily clear to the translator whether the translation for plane should be avion (airplane) or plan (geometric plane). The translator might not ever see the actual software with their translated text.

[derefr]

The convenient thing in this case (verification of translation of academic papers from the speaker's native language to English) is that the authors of the paper likely already 1. can read English to some degree, and 2. are highly likely to be familiar specifically with the jargon terms of their field in both their own language and in English.

This is because, even in countries with a different primary spoken language, many academic subjects, especially at a graduate level (masters/PhD programs — i.e. when publishing starts to matter), are still taught at universities at least partly in English. The best textbooks are usually written in English (with acceptably-faithful translations of these texts being rarer than you'd think); all the seminal papers one might reference are likely to be in English; etc. For many programs, the ability to read English to some degree is a requirement for attendance.

And yet these same programs are also likely to provide lectures (and TA assistance) in the country's own native language, with the native-language versions of the jargon terms used. And any collaborative work is likely to also occur in the native language. So attendees of such programs end up exposed to both the native-language and English-language terms within their field.

This means that academics in these places often have very little trouble in verifying the fidelity of translation of the jargon in their papers. It's usually all the other stuff in the translation that they aren't sure is correct. But this can be cheaply verified by handing the paper to any fluently-multilingual non-academic and asking them to check the translation, with the instruction to just ignore the jargon terms because they were already verified.

[gus_massa]

> with the native-language versions of the jargon terms used

It depends on the country. Here in Argentina we use a lot of loaned words for technical terms, but I think in Spain they like to translate everything.

[neves]

When reading technical material in my native language, I sometimes need to translate it back to English to fully understand it.

[Davidzheng]

idk I think Gemini 2.5 did a great job at almost all research math papers translating from french to english...

[noooooooph]

To that point I think it's lovely how LLMs democratize science. At ICLR a few years ago I spoke with a few Korean researchers that were delighted that their relative inability to write in English was no being held against them during the review process. I think until then I underestimated how pivotal this technology was in lowering the barrier to entry for the non-English speaking scientific community.

[melagonster]

If they can write a whole draft in their first language, they can easily read the translated English version and correct it. The errors described by gp/op were generated when authors directly required LLM to generate a full paragraph of text. Look at my terrible English; I really have the experience of the full process from draft to English version before :)

[abbassix]

We still do not have a standardized way to represent Machine Learning concepts. For example in vision model, I see lots of papers confused about the "skip connections" and "residual connection" and when they concatenate channels they call them "residual connection" while it shows that they haven't understood why we call them "residual" in the first place. In my humble opinion, each conference, and better be a confederation of conferences, work together to provide a glossary, a technical guideline, and also a special machine translation tool, to correct a non-clear-with-lots-of-grammatical-error-English like mine!

[neves]

I'm surprised by these results. I agree that LLMs are a great tool for offsetting the English-speaking world's advantage. I would have expected non-Anglo-American universities to rank at the top of the list. One of the most valuable features of LLMs from the beginning has been their ability to improve written language.

Why is their use more intense in English-speaking universities?

[neilv]

Good point. There may be a place for LLMs for science writing translation (hopefully not adding nor subtracting anything) when you're not fluent in the language of a venue.

You need a way to validate the correctness of the translation, and to be able to stand behind whatever the translation says. And the translation should be disclosed on the paper.

[bjourne]

There are legitimate, non-cheating ways to use LLMs for writing. I often use the wrong verb forms ("They synthesizes the ..."), write "though" when it should be "although", and forget to comma-separate clauses. LLMs are perfect for that. Generating text from scratch, however, is wrong.

[thaumasiotes]

> I often ... write "though" when it should be "although"

That is a purely imaginary "error". Anywhere you can use 'although', you are free to use 'though' instead.

[bjourne]

Yeah, but you cannot use although anywhere you can use though, though.

[thaumasiotes]

That's true, but the one-way substitutability still means there is no such thing as "writ[ing] 'though' when it should be 'although'".

[bloppe]

I agree, but I don't think any of the broadly acceptable uses would result in easily identifiable flaws like those in the post, especially hallucinated URLs.

[rustystump]

I do similar proofing (esp spelling) but u need to be very careful as it will nudge u to specific styles that rob originality.

[piyh]

>I am troubled by people using an LLM at all to write academic research papers.

I'm an outsider to the academic system. I have cool projects that I feel push some niche application to SOTA in my tiny little domain, which is publishable based on many of the papers I've read.

If I can build a system that does a thing, I can benchmark and prove it's better than previous papers, my main blocker is getting all my work and information into the "Arxiv PDF" format and tone. Seems like a good use of LLMs to me.

[thomasahle]

> And also plagiarism, when you claim authorship of it.

I don't actually mind putting Claude as a co-author on my github commits.

But for papers there are usually so many tools involved. It would be crowded to include each of Claude, Gemini, Codex, Mathematica, Grammarly, Translate etc. as co-authors, even though I used all of them for some parts.

Maybe just having a "tools used" section could work?

[the__alchemist]

I suspect the parent post was concerned about plagiarizing the author of training data; not software tools.

[mapontosevenths]

> It's a shoddy, irresponsible way to work. And also plagiarism, when you claim authorship of it.

It reminds me of kids these days and their fancy calculators! Those new fangled doohickeys just aren't reliable, and the kids never realize that they won't always have a calculator on them! Everyone should just do it the good old fashioned way with slide rules!

Or these darn kids and their unreliable sources like Wikipedia! Everyone knows that you need a nice solid reliable source that's made out of dead trees and fact checked but up to 3 paid professionals!

[usefulcat]

I doubt that it's common for anyone to read a research paper and then question whether the researcher's calculator was working reliably.

Sure, maybe someday LLMs will be able to report facts in a mostly reliable fashion (like a typical calculator), but we're definitely not even close to that yet, so until we are the skepticism is very much warranted. Especially when the details really do matter, as in scientific research.

[westurner]

> I doubt that it's common for anyone to read a research paper and then question whether the researcher's calculator was working reliably

Reproducibility and repeatability in the sciences?

Replication crisis > Causes > Problems with the publication system in science > Mathematical errors; Causes > Questionable research practices > In AI research, Remedies > [..., open science, reproducible workflows, disclosure, ] https://en.wikipedia.org/wiki/Replication_crisis#Mathematica...

Already verifiable proofs are too impossibly many pages for human review

There are "verify each Premise" and "verify the logical form of the Argument" (P therefore Q) steps that still the model doesn't do for the user.

For your domain, how insufficient is the output given process as a prompt like:

Identify hallucinations from models prior to (date in the future)

Check each sentence of this: ```{...}```

Research ScholarlyArticles (and then their Datasets) which support and which reject your conclusions. Critically review findings and controls.

Suggest code to write to apply data science principles to proving correlative and causative relations given already-collected observations.

Design experiment(s) given the scientific method to statistically prove causative (and also correlative) relations

Identify a meta-analytic workflow (process, tools, schema, and maybe code) for proving what is suggested by this chat

[mapontosevenths]

> whether the researcher's calculator was working reliably.

LLM's do not work reliably, that's not their purpose.

If you use them that way it's akin to using a butter knife as a screwdriver. You might get away with it once or twice, but then you slip and stab yourself. Better to go find screwdriver if you need reliable.

[foxes]

Im really not motivated by this argument; it seems a false equivalence. Its not merely a spell checker or removing some tedium.

As a professional mathematician I used wikipedia all the time to lookup quick facts before verifying it myself or elsewhere. A calculator well; I can use an actual programming language.

Up until this point neither of those tools were asvertised or used by people to entirely replace human input.

[ekidd]

There are some interesting possibilities for LLMs in math, especially in terms of generating machine-checked proofs using languages like Lean. But this is a supplement to the actual result, where the LLM would actually be adding a more rigorous version of a human's argument with all the boring steps included.

In a few cases, I see Terrance Tao has pointed out examples LLMs actually finding proofs of open problems unassisted. Not necessarily problems anyone cared deeply about. But there's still the fact that if the proof holds, then it's valid no matter who or what came up with it.

So it's complicated I guess?

[sodapopcan]

I hate to sound like a 19 year old on Reddit but:

AI People: "AI is a completely unprecedented technology where its introduction is unlike the introduction of any other transformative technology in history! We must treat it totally differently!"

Also AI People: "You're worried about nothing, this is just like when people were worried about the internet."

[mikkupikku]

The internet analogy is apt because it was in fact a massive bubble, but that bubble popping didn't mean the tech went away. Same will happen again, which is a point both extremes miss. One would have you believe there is no bubble and you should dump all your money into this industry, while the other would have us believe that once the bubble pops all this AI stuff will be debunked and discarded as useless scamware.

[foxes]

Well the internet has definitely changed things; but also it wasnt initially controlled by a bunch of megacorps with the same level of power and centralisation today.

[sodapopcan]

:pointing-up-emoji:

[andrepd]

> Those new fangled doohickeys just aren't reliable

Except they are (unlike a chatbot, a calculator is perfectly deterministic), and the unreliability of LLMs is one of their most, if not the most, widespread target of criticism.

Low effort doesn't even begin to describe your comment.

[jama211]

As low effort as you hand waving away any nuance because it doesn’t agree with you?

[mapontosevenths]

> Except they are (unlike a chatbot, a calculator is perfectly deterministic)

LLM's are supposed to be stochastic. That is not a bug, I can see why you find that disappointing but it's just the reality of the tool.

However, as I mentioned elsewhere calculators also have bugs and those bugs make their way into scientific research all the time. Floating point errors are particularly common, as are order of operations problems because physical devices get it wrong frequently and are hard to patch. Worse, they are not SUPPOSED TO BE stochastic so when they fail nobody notices until it's far too late. [0 - PDF]

Further, spreadsheets are no better, for example a scan of ~3,600 genomics papers found that about 1 in 5 had gene‑name errors (e.g., SEPT2 → “2‑Sep”) because that's how Excel likes to format things.[1] Again, this is much worse than a stochastic machine doing it's stochastic job... because it's not SUPPOSED to be random, it's just broken and on a truly massive scale.

[0] https://ttu-ir.tdl.org/server/api/core/bitstreams/7fce5b73-1...

[1]https://www.washingtonpost.com/news/wonk/wp/2016/08/26/an-al...

[raddan]

That’s a strange argument. There are plenty of stochastic processes that have perfectly acceptable guarantees. A good example is Karger’s min-cut algorithm. You might not know what you get on any given single run, but you know EXACTLY what you’re going to get when you crank up the number of trials.

Nobody can tell you what you are going to get when you run an LLM once. Nobody can tell you what you’re going to get when you run it N times. There are, in fact, no guarantees at all. Nobody even really knows why it can solve some problems and why it can’t solve other except maybe it memorized the answer at some point. But this is not how they are marketed.

They are marketed as wondrous inventions that can SOLVE EVERYTHING. This is obviously not true. You can verify it yourself, with a simple deterministic problem: generate an arithmetic expression of length N. As you increase N, the probability that an LLM can solve it drops to zero.

Ok, fine. This kind of problem is not a good fit for an LLM. But which is? And after you’ve found a problem that seems like a good fit, how do you know? Did you test it systematically? The big LLM vendors are fudging the numbers. They’re testing on the training set, they’re using ad hoc measurements and so on. But don’t take my word for it. There’s lots of great literature out there that probes the eccentricities of these models; for some reason this work rarely makes its way into the HN echo chamber.

Now I’m not saying these things are broken and useless. Far from it. I use them every day. But I don’t trust anything they produce, because there are no guarantees, and I have been burned many times. If you have not been burned, you’re either exceptionally lucky, you are asking it to solve homework assignments, or you are ignoring the pain.

Excel bugs are not the same thing. Most of those problems can be found trivially. You can find them because Excel is a language with clear rules (just not clear to those particular users). The problem with Excel is that people aren’t looking for bugs.

[mapontosevenths]

> But I don’t trust anything they produce, because there are no guarantees

> Did you test it systematically?

Yes! That is exactly the right way to use them. For example, when I'm vibe coding I don't ask it to write code. I ask it to write unit tests. THEN I verify that the test is actually testing for the right things with my own eyeballs. THEN I ask it to write code that passes the unit tests.

Same with even text formatting. Sometimes I ask it to write a pydantic script to validate text inputs of "x" format. Often writing the text to specify the format is itself a major undertaking. Then once the script is working I ask for the text, and tell it to use the script to validate it. After that I can know that I can expect deterministic results, though it often takes a few tries for it to pass the validator.

You CAN get deterministic results, you just have to adapt your expectations to match what the tool is capable of instead of expecting your hammer to magically be a great screwdriver.

I do agree that the SOLVE EVERYTHING crowd are severely misguided, but so are the SOLVE NOTHING crowd. It's a tool, just use it properly and all will be well.

[api]

One issue with this analogy is that calculators really are precise when used correctly. LLMs are not.

I do think they can be used in research but not without careful checking. In my own work I’ve found them most useful as search aids and brainstorming sounding boards.

[mapontosevenths]

> I do think they can be used in research but not without careful checking.

Of course you are right. It is the same with all tools, calculators included, if you use them improperly you get poor results.

In this case they're stochastic, which isn't something people are used to happening with computers yet. You have to understand that and learn how to use them or you will get poor results.

[mapontosevenths]

> One issue with this analogy is that calculators really are precise when used correctly. LLMs are not.

I made this a separate comment, because it's wildly off topic, but... they actually aren't. Especially for very large numbers or for high precision. When's the last time you did a firmware update on yours?

It's fairly trivial to find lists of calculator flaws and then identify them in research papers. I recall reading a research paper about it in the 00's.

[ragnarok451]

One issue with this analogy is that paper encyclopedias really are precise when used correctly. Wikipedia is not.

I do think it can be used in research but not without careful checking. In my own work I've found it most useful as a search aid and for brainstorming.

^ this same comment 10 years ago

[mikkupikku]

Paper encyclopedias were neither precise nor accurate. You could count on them to give you ballpark figures most of the time, but certainly not precise answers. And that's assuming the set was new, but in reality most encyclopedias ever encountered by people in reality were several years old at least. I remember the encyclopedia set I had access to in the 90s was written before the USSR fell..

[mapontosevenths]

> I do think it can be used in research but not without careful checking.

This is really just restating what I already said in this thread, but you're right. That's because wikipedia isn't a primary source and was never, ever meant to be. You are SUPPOSED to go read it then click through to the primary sources and cite those.

Lots of people use it incorrectly and get bad results because they still haven't realized this... all these years later.

Same thing with treating stochastic LLM's like sources of truth and knowledge. Those folks are just doing it wrong.

[neilv]

Annoying dismissal.

In an academic paper, you condense a lot of thinking and work, into a writeup.

Why would you blow off the writeup part, and impose AI slop upon the reviewers and the research community?

[HKH2]

I don't necessarily disagree, but researchers are not required to be good communicators. An academic can lead their field and be a terrible lecturer. A specialist can let a generalist help explain concepts for them.

They should still review the final result though. There is no excuse for not doing that.

[dasyud]

I disagree here. A good researcher has to be a good communicator. I am not saying that it is necessarily the case that you don't understand the topic if you cannot explain it well enough to someone new, but it is essential to communicate to have a good exchange of ideas with others, and consequently, become a better researcher. This is one of the skills you learn in a PhD program.

[HKH2]

That is how it should be, yes. Do PhDs always meet that standard though? No.

[aydyn]

>also plagiarism

To me, this is a reminder of how much of a specific minority this forum is.

Nobody I know in real life, personally or at work, has expressed this belief.

I have literally only ever encountered this anti-AI extremism (extremism in the non-pejorative sense) in places like reddit and here.

Clearly, the authors in NeurIPS don't agree that using an LLM to help write is "plagiarism", and I would trust their opinions far more than some random redditor.

[BobbyJo]

> Nobody I know in real life, personally or at work, has expressed this belief.

TBF, most people in real life don't even know how AI works to any degree, so using that as an argument that parent's opinion is extreme is kind of circular reasoning.

> I have literally only ever encountered this anti-AI extremism (extremism in the non-pejorative sense) in places like reddit and here.

I don't see parent's opinions as anti-AI. It's more an argument about what AI is currently, and what research is supposed to be. AI is existing ideas. Research is supposed to be new ideas. If much of your research paper can be written by AI, I call into question whether or not it represents actual research.

[michaelt]

> Research is supposed to be new ideas. If much of your research paper can be written by AI, I call into question whether or not it represents actual research.

One would hope the authors are forming a hypothesis, performing an experiment, gathering and analysing results, and only then passing it to the AI to convert it into a paper.

If I have a theory that, IDK, laser welds in a sine wave pattern are stronger than laser welds in a zigzag pattern - I've still got to design the exact experimental details, obtain all the equipment and consumables, cut a few dozen test coupons, weld them, strength test them, and record all the measurements.

Obviously if I skipped the experimentation and just had an AI fabricate the results table, that's academic misconduct of the clearest form.

[BobbyJo]

I am not an academic, so correct me if I am wrong, but in your example, the actual writing would probably only represent a small fraction of the time spent. Is it even worth using AI for anything other than spelling and grammar correction at that point? I think using an LLM to generate a paper from high level points wouldn't save much, if any, time if it was then reviewed the way that would require.

My brother in law is a professor, and he has a pretty bad opinion of colleagues that use LLMs to write papers, as his field (economics) doesn't involve much experimentation, and instead relies on data analysis, simulation, and reasoning. It seemed to me like the LLM assisted papers that he's seen have mostly been pretty low impact filler papers.

[aydyn]

> I am not an academic, so correct me if I am wrong, but in your example, the actual writing would probably only represent a small fraction of the time spent. Is it even worth using AI for anything other than spelling and grammar correction at that point? I think using an LLM to generate a paper from high level points wouldn't save much, if any, time if it was then reviewed the way that would require.

Its understandable that you believe that, but its absolutely true that writing in academia is a huge time sink. Think about it, the first thing your reviewers are going to notice is not results but how well it is written.

If its written terribly you have lost, and it doesnt matter how good your results are at that point. Its common to spend days with your PI writing a paper to perfection, and then spend months back and forth with reviewers updating and improving the text. This is even more true the higher up you go in journal prestige.

[aydyn]

> TBF, most people in real life don't even know how AI works to any degree

How about the authors who do research for NeurIPS? Do they know how AI works?

[Intermernet]

Who knows? Do NeurIPS have a pedigree of original, well sourced research dating back to before the advent of LLMs? We're at the point where both of the terms "AI" and "Experts" are so blurred it's almost impossible to trust or distrust anything without spending more time on due diligence than most subjects deserve.

As the wise woman once said "Ain't nobody got time for that".

[Davidzheng]

"If much of your research paper can be written by AI, I call into question whether or not it represents actual research" And what happens to this statement if next year or later this year the papers that can be autonomously written passes median human paper mark?

[BobbyJo]

What does it mean to cross the median human paper mark? How os that measured?

It seems to me like most of the LLM benchmarks wind up being gamed. So, even if there were a good benchmark there, which I do not believe there is, the validity of the benchmark would likely diminish pretty quickly.

[fingerlocks]

I find that hard to believe. Every creative professional that I know shares this sentiment. That’s several graphic designers at big tech companies, one person in print media, and one visual effects artist in the film industry. And once you include many of their professional colleagues that becomes a decent sample size.

[jama211]

Graphic design is a completely different kettle of fish. Comparing it to academic paper writing is disingenuous.

[kortilla]

The thread is about not knowing anyone at all who thinks AI is plagiarizing.

[jama211]

Yes, plagiarising text based content. No one in this thread meant graphics.

[nick238]

The LLM model and version should be included as an author so there's useful information about where the content came from.

[neilv]

> AI Overview

> Plagiarism is using someone else's words, ideas, or work as your own without proper credit, a serious breach of ethics leading to academic failure, job loss, or legal issues, and can range from copying text (direct) to paraphrasing without citation (mosaic), often detected by software and best avoided by meticulous citation, quoting, and paraphrasing to show original thought and attribution.

[aydyn]

Not sure if I am correctly interpreting your implicit point but

> Plagiarism is using someone else's words,

Its right there. LLM is not "someone else"; its a very useful piece of software.

[Culonavirus]

Higher education is not free. People pay a shit ton of money to attend and also governments (taxpayers) invest a lot. Imagine offloading your research to an AI bot...

[falkensmaize]

“Anti-AI extremism”? Seriously?

Where does this bizarre impulse to dogmatically defend LLM output come from? I don’t understand it.

If AI is a reliable and quality tool, that will become evident without the need to defend it - it’s got billions (trillions?) of dollars backstopping it. The skeptical pushback is WAY more important right now than the optimistic embrace.

[cthalupa]

The fact that there is absurd AI hype right now doesn't mean that we should let equally absurd bullshit pass on the other side of the spectrum. Having a reasonable and accurate discussion about the benefits, drawbacks, side effects, etc. is WAY more important right now than being flagrantly incorrect in either direction.

Meanwhile this entire comment thread is about what appears to be, as fumi2026 points out in their comment, a predatory marketing play by a startup hoping to capitalize on the exact sort of anti AI sentiment that you seem to think is important... just because there is pro AI sentiment?

Naming and shaming everyday researchers based on the idea that they have let hallucinations slip into their paper all because your own AI model has decided thatit was AI so you can signal boost your product seems pretty shitty and exploitative to me, and is only viable as a product and marketing strategy because of the visceral anti AI sentiment in some places.

[falkensmaize]

“anti-ai sentiment”

No that’s a straw man, sorry. Skepticism is not the same thing as irrational rejection. It means that I don’t believe you until you’ve proven with evidence that what you’re saying is true.

The efficacy and reliability of LLMs requires proof. Ai companies are pouring extraordinary, unprecedented amounts of money into promoting the idea that their products are intelligent and trustworthy. That marketing push absolutely dwarfs the skeptical voices and that’s what makes those voices more important at the moment. If the researchers named have claims made against them that aren’t true, that should be a pretty easy thing for them to refute.

[rustystump]

The cat is out of the bag tho. AI does have provably crazy value. Certainly not the agi hype marketing spews and who knows how economically viable it would be without vc.

However, i think any one who is still skeptical of the real efficacy is willfully ignorant. This is not a moral endorsement on how it was made or if it is moral to use but god damn it is a game changer across vast domains.

[necovek]

There were a number of studies already shared reporting on the impression of increased efficiency without the actual increase in efficiency.

Which means that it's still not a given, though there are obviously cases where individual cases seem to be good proof of it.

[cthalupa]

There was a front page post just a couple of days ago where the article claimed LLMs have not improved in any way in over a year - an obviously absurd statement. A year before Opus 4.5, I couldn't get models to spit out a one shot Tampermonkey script to add chapter turns to my arrow keys. Now I can one small personal projects in claude code.

If you are saying that people are not making irrational and intellectually dishonest arguments about AI, I can't believe that we're reading the same articles and same comments.

[techpression]

Isn’t that the whole point of publishing? This happened plenty before AI too, and the claims are easily verified by checking the claimed hallucinations. Don’t publish things that aren’t verified and you won’t have a problem, same as before but perhaps now it’s easier to verify, which is a good thing. We see this problem in many areas, last week it was a criminal case where a made up law was referenced, luckily the judge knew to call it out. We can’t just blindly trust things in this era, and calling it out is the only way to bring it up to the surface.

[cthalupa]

> Isn’t that the whole point of publishing?

No, obviously not. You're confusing a marketing post by people with a product to sell with an actual review of the work by the relevant community, or even review by interested laypeople.

This is a marketing post where they provide no evidence that any of these are hallucinations beyond their own AI tool telling them so - and how do we know it isn't hallucinating? Are there hallucinations in there? Almost certainly. Would the authors deserve being called out by people reviewing their work? Sure.

But what people don't deserve is an unrelated VC funded tech company jumping in and claiming all of their errors are LLM hallucinations when they have no actual proof, painting them all a certain way so they can sell their product.

> Don’t publish things that aren’t verified and you won’t have a problem

If we were holding this company to the same standard, this blog wouldn't be posted either. They have not and can not verify their claims - they can't even say that their claims are based on their own investigations.

[techpression]

Most research is funded by someone with a product to sell, not all but a frightening amount of it. VC to sell, VC to review. The burden of proof is always on the one publishing and it can be a very frustrating experience, but that is how it is, the one making the claim needs to defend themselves, from people (who can be a very big hit or miss) or machines alike. The good thing is that if this product is crap then it will quickly disappear.

[jama211]

Yup, and no matter how flimsy an anti-ai article is, it will skyrocket to the top of HN because of it. It makes sense though, HN users are the most likely to feel threatened by LLMs, and therefore are more likely to be anxious about them.

I don’t love ai either, but that’s the truth.

[techpression]

Strange, I find it quite the opposite, especially ”pro-ai” comments are often top of the list.

[jama211]

I think there’s a bit of both, with a valley in the middle

[neilv]

> Clearly, the authors in NeurIPS don't agree that using an LLM to help write is "plagiarism",

Or they didn't consider that it arguably fell within academia's definition of plagiarism.

Or they thought they could get away with it.

Why is someone behaving questionably the authority on whether that's OK?

> Nobody I know in real life, personally or at work, has expressed this belief. I have literally only ever encountered this anti-AI extremism (extremism in the non-pejorative sense) in places like reddit and here.

It's not "anti-AI extremism".

If no one you know has said, "Hey, wait a minute, if I'm copy&pasting this text I didn't write, and putting my name on it, without credit or attribution, isn't that like... no... what am I missing?" then maybe they are focused on other angles.

That doesn't mean that people who consider different angles than your friends do are "extremist".

They're only "extremist" in the way that anyone critical at all of 'crypto' was "extremist", to the bros pumping it. Not coincidentally, there's some overlap in bros between the two.

[aydyn]

> Why is someone behaving questionably the authority on whether that's OK?

Because they are not. Using AI to help writing is something literally every company is pushing for.

[tsimionescu]

How is that relevant? Companies care very little about plagiarism, at least in the ethical sense (they do care if they think it's a legal risk, but that has turned out to not be the case with AI, so far at least).

[aydyn]

What do you mean how is that relevant? Its a vast majority opinion in society that using ai to help you write is fine. Calling it "plagiarism" is a tiny minority online opinion.

[tsimionescu]

First of all, the very fact that companies need to encourage it shows that it is not already a majority opinion in society, it is a majority opinion among company management, which is often extremely unethical.

Secondly, even if it is true that it is a majority opinion in society doesn't mean it's right. Society at large often misunderstands how technology works and what risks it brings and what are its inevitable downstream effects. It was a majority opinion in society for decades or centuries that smoking is neutral to your health - that doesn't mean they were right.

[necovek]

As long as AI companies have paid them to train on their data (see a number of licensing deals between OpenAI and news agencies and such).