Posted by dopamine_daddy 14 hours ago
I also uploaded my PhD dissertation from 2012 and got back 40% machine, which is just barely below the 42% threshold.
I don't publish anymore... but does this mean I wrote like a LLM or did LLMs learn from me? :p
Update: I also uploaded a IEEE CLUSTERS paper I wrote in 2015 and it came back 74% machine written :|
A well established user here was adamant that I used AI because apparently humans never ever wrote proofs.
This was a while ago. Any well crafted human output is now being dismissively cast as AI if the reader is challenged by the output intellectually/politically.
I'm not convinced we'll ever have full-proof detectors, and certainly the false-positive rate will make them irresponsible for accusations of intellectual/academic fraud, I do think that LLMs are easy for folks to sniff out on average so I imagine it's possible to detect many instances.
Pangram's detector is anecdotally very accurate in my tests. This detector appears to be fine-tuned on a very small dataset (200 papers per subject), and suspect the problem might be in part that.
It's just text.
The biggest results: in Jan of 2026 about 39% of papers got flagged as AI written. In computer science speicifcally the peak was at 65%. Mathematics barely moved away from 0.7%, though the proof heavy math texts might just not get picked up by the detector properly.
All this is a detector estimate of a statistical signal and not a proof any given author used AI. Machine written can also mean heavy AI-assisted editing.
* pre-chatGPT is not an effective control because language evolves. In the arxiv corpus in particular there are "fashions" in research depending on what gets funded lately, not to mention many new words and topics not invented before a given paper.
* In general, detecting AI from content seems difficult as humans write like they read. To the extent there are unique factors recognizable as AI and to the extent humans read them, they will eventually incorporate them into their writing style. Accordingly, you'd need to model a rolling window of "AI tells" that decay at some rate.
FYI this is all relatively new so there might be lots of issues and iterations coming.
I had to change my mind on AI detectors after playing around with it.
It would be interesting to hear how this detector compares. It also seems to be aiming for low fp rate.
It's hard to say if this code is structurally better or worse than before, but it's certainly voluminous and as far as leadership could ever tell, with their flawed metrics, that is all that matters. It will be years before we figure out if this is a good idea and worth the cognitive atrophy.
Anyone not using LLMs all day is just not going to be as prolific. I can't imagine that the same factors aren't at play in the scientific research community where it's all about how much you can publish.
I can't speak for the entire scientific research community, but I can say that for basic biomedical research (not clinical research), LLMs are mostly ignored. They simply don't have the ability to parse most raw wet lab data.
This obviously has not stopped the creation of a legion of startups, new Asst Profs, etc, claiming that they are using "AI" to crack new frontiers. In practice, the most effective of these use machine learning, rather than LLMs.
Hah. I've been working with ChatGPT-5.6 Sol and a lot of guidance to port a particular geometry from 2800 lines of Python-scripted Rhino3D (basically a custom parametric CAD kernel in there, plus use of it) to Python-scripted OCCT/FreeCAD (an existing CAD kernel) and it's up to 36,000 lines and only about half way there. And I've been setting goals and targets for duplication minimization, code size reduction, etc. The results are fine for my purposes, but if I put a positive value on "voluminous" I'd be super impressed; with my actual negative value assignment on lines of code, this is more "absolute crap but still useful to me."
I think "absolute crap but still useful to me" is a pretty high value and not worth neglecting, but I'm definitely scared by leadership who sees the toilet overflowng and assumes that means that value has been maximized.
If I extrapolate this example to my professional life, this code now manages millions of dollars, a single mistake can wipe it all out, it has to be maintained by 5 other engineers and understood by 5 other domain experts.
Huh?
But that's easy to tell?
It's better then incompetents code, worse then a motivated average dev... But good enough hence the real question is value aka time& money invested/quality.
That's much harder to tell, and I currently think opus/fable generated code is decent enough to be safely in favor - at least on subscription
That's well put.
> But good enough hence the real question is value aka time& money invested/quality.
There's time invested SO FAR and time that will have to be invested to maintain it. In my experience, even with Fable, it's not there yet. It's the reason why it's easy to vibe code an app from scratch, but at some point when complexity significantly increases, the codebase becomes a mess.
This was my big fear before we saw price increases. Now I'm pinning all my hopes on AI being too expensive to justify further big corporate pushes. (Sigh.) I love having new tools, but I hate being pushed to use ______ tool to meet some managerial metric.
Can you unpack that a bit? It produces measurably, meaningfully inferior code everywhere I see it in use.
> "leadership encourages this because from what they can tell, there is no downside"
As said 'leadership' I find this a bit puzzling. I'm seeing strong, quantifiable evidence of increasing churn, increasing incident count, and length of downtime from the date of our biggest push into GenAI, and I'm organizing efforts on my teams to mitigate those issues and actively reduce GenAI adoption.
If you mean my c-suite, you're mostly correct although they are already rumbling about seeing zero or negative ROI on GenAI investments.
> Anyone not using LLMs all day is just not going to be as prolific
Agreed, but prolific != productive.
Not sure if its just me, lately I have started feeling pretty offensive about the increased usage of the word. Its management not leadership by any means.
> "leadership encourages this because from what they can tell, there is no downside"
For most people in management its easier to pick the current set of slangs/abbreviation's, general trend and go with it. Understanding the details would take time, raise questions and no one in management has time or political capital to spend on it.
Humans write slop too, you know. Just saying.
I've also uploaded text samples from my own (unreleased) research from pre-LLM era, and it's seemingly scoring pretty high on the LLM-detection scores. On other papers, nearly every sentence is highlighted as red "machine-leaning," but that does not impact the score? Additionally, there are dramatic differences between the scores for identical text with and without LaTeX formatting, despite the fact that it should not matter.
The takeaway from this should be "it is difficult to detect generated text and we should be careful about accepting results simply because they confirm a hypothesis."
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Relatedly, the text above scores as highly machine-written, despite the fact that I just wrote it with my human hands, I promise :)
A more interesting question, to me, is looking at a corpus of essays and analyzing how writing has changed with the introduction of LLMs. We can look at changes in vocabulary, linguistic features, style embeddings, regular embeddings, typos, errors, and references over time. When looked at in this way it is clear that academic writing has changed at the population level but what has led the change is harder to track down.
Maybe for very short phrases, but otherwise I disagree. Phrasing very quickly runs into a combinatorial explosion. In the words of Noam Chomsky, "Virtually every sentence that a person utters or understands is a brand-new combination of words, appearing for the first time in the history of the universe."
In my opinion, the difficulty in LLM/human text discrimination isn't that a person might coincidentally write exactly the same text as an LLM would, but rather that 1) LLMs aren't hard locked to a single phrasing (so this is a tougher problem than matching to a single static document, e.g. plagiarism detection) and 2) text has relatively low information density, so you need quite a bit of it to gather enough data to run a statistical test with a reasonably narrow confidence interval.
When 65% of the papers you read have the characteristics of being AI written, whether or not you use AI to write, your writing will be influenced by the AI style. I imagine this must be particularly the case for newbie researchers who are still developing their writing style
there's also the fact that "the AI style" has changed over time. for instance, the word "delve" is notorious as an "AI sign," which it was up until mid-2024, at which point it dropped off sharply and has now basically disappeared from LLM output. so if someone happened to pick that up due to reading it everywhere, their writing is now less characteristic of AI, not more.
To me it sounds like 1. Either your tool is just not that good and reliable as you thought, 2. AI is trained on human written articles, so some of that human written content informed the now established “AI slop”.
There are people who shipped “slop” before AI.
The funny thing is that "slop" was defined by the writing habits of AI model, which we have learned to pick upon and recognize.
The "It's not X, it's Y", the rhetorical questions and other patterns would have been the tools of a skilled writer, and those people writing "like AI" before AI most likely would have been recognized as such.
It is not because we ask LLM to write us a paper about X. It is because we are bad at writing in a scientific style, and american editors expect us to do it. With LLMs, we can write in basic sentences and tell the LLM the idea and it converts that to nice writing.
If you write each paragraph and have an LLM make that paragraph more scientific, it is entirely your paper, but it is flagged as LLM generated. If you have an LLM write the paper but speak good enough English, you can make it look human even though it is not human.
I don't think this is surprising. Good technical writing is very precise. If you're starting from non-technical writing, I suspect that in most cases you can't make it "sound scientific" without adding new claims or changing the meaning. (Maybe you are being more careful, but this is something that worries me in general.)
There really is no point, as long as you verify the content matches your intent and edit out anything poorly written.
Frankly, I've read plenty of papers by native English-speakers over the years that'd strongly benefit from being rewritten by an LLM too...
This is a sin.
Here's a paper by a non native English speaker. The lack of the formal style doesn't cause any issue. Preciseness is what matters.
"More scientific" is not some merely stylistic thing that faithfully preserves the original meaning of what you wrote. The precise details of each paragraph matters a lot in terms of what and how it communicates. The fact that these details do matter means that, according to my accounting, it is not entirely your paper.
Also, I find striving for "scientific" to be a pretty undesirable thing. Why should papers read like that? What is the benefit? The best papers (in terms of their writing and communication) are unpretentious and conversational. I'm pretty sure I'd prefer your "basic sentences", especially if they were wholly yours. (I understand that there are also external forces at play here as you mentioned.)
But I suspect that a lot of academic's feelings about it are informed by what others have told them and how they've been trained, rather than by what's actually permissible in the publishing system.
This paper was written using AI, to be honest
Perhaps that’s what’s happening.
Sure, some people are artists - but most aren't.
For example, LLMs love to talk about LLMs (and the people who write with LLMs love to write about LLMs). Could "large language model" itself therefore be flagged as an AI-like phrase by this approach? It didn't exist much in the literature before 2022, does now, and certainly does more in AI-generated text: but, it is not actually a great way to distinguish modern AI generated text from human written text.
A helpful control would be to show that on some cohort of papers that can be declared reasonably clean of LLM generated text post 2023 there are very low rates compared to the arxiv.
For example, while papers in the journals Nature and Science are unlikely to be entirely LLM free at this point, if those were tested through 2026, we should see a line significantly lower than the arxiv's growth.
The difficulty with this is then: How do you get a clean post 2023 dataset? I have no straightforward idea for this. You can't use other AI detectors to build it because then you'd never outperform them.