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How we measured AI writing across arXiv, and where the measurement breaks
121–130 of 185 posts
Re: How we measured AI writing across arXiv, and where the measurement breaks
#122I run most professional statements and articles through LLMs before submission. This helps correct grammar and improves accessibility through better syntax (because I write exactly how I think). The article doesn't seem to mention consideration of AI for polishing human work.
What was ever wrong with just producing a second draft of your work?
I had an LLM that could do it for me. Cheaper, faster, better. And yes, I re-edit parts I want afterwards.
Re: How we measured AI writing across arXiv, and where the measurement breaks
#123There some real game theory mechanics at play when it comes to LLM usage in corporations. Devs are cranking out superficially superior code and documentation by just aiming the Claude Code fire hose at everything they can. Leadership encourages this because from what they can tell, there is no downside. It's hard to say if this code is structurally better or worse than before, but it's certainly voluminous and as far…
> 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. 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-s…
> I think "absolute crap but still useful to me" is a pretty high value and not worth neglecting
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.
Re: How we measured AI writing across arXiv, and where the measurement breaks
#124There some real game theory mechanics at play when it comes to LLM usage in corporations. Devs are cranking out superficially superior code and documentation by just aiming the Claude Code fire hose at everything they can. Leadership encourages this because from what they can tell, there is no downside. It's hard to say if this code is structurally better or worse than before, but it's certainly voluminous and as far…
> It's hard to say if this code is structurally better or worse than before, 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
> It's better then incompetents code, worse then a motivated average dev...
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.
Re: How we measured AI writing across arXiv, and where the measurement breaks
#125Earlier quoted context omitted.
I think the key question is more effective at what? I see plenty of anecdotal evidence that models have been trained fantastically well—and getting better—at writing to trigger the right neurons in the human population to produce “This is interesting/informative/correct” responses in bulk. Could their ability to produce those responses run far ahead of their ability to actually achieve the last in reality? Sure seems…
Many people are bad at writing, including scientists. Improvements could be articulating a concept in a way that the reader will understand it clearly.
My hypothesis is that an LLM’s ability to concoct prose that will convince even an expert of the validity of an idea is largely independent of the LLM’s ability to validate the idea itself, or whether the idea is correct in the first place. Especially if it’s being prompted to do the former, not the latter.
My anecdotal evidence is the LLM-generated, inchoate technical dross that is routinely upvoted onto the hn front page. Much of it isn’t even coherent enough to be wrong, but the readership here finds it interesting!
Re: How we measured AI writing across arXiv, and where the measurement breaks
#126There some real game theory mechanics at play when it comes to LLM usage in corporations. Devs are cranking out superficially superior code and documentation by just aiming the Claude Code fire hose at everything they can. Leadership encourages this because from what they can tell, there is no downside. It's hard to say if this code is structurally better or worse than before, but it's certainly voluminous and as far…
IMO, cognitive athrophy is the point. If humans become dependent on AI, those corporations will get enough leverage to change laws in their favor. For example, people don't want a huge data center in the middle of their city, but the corporations will say that the same people can't do anything without AI powered by this data center. That's why I think that making humans deficient and dependent on AI is the entire poi…
This is just conspiracy nonsense.
Re: How we measured AI writing across arXiv, and where the measurement breaks
#127I've looked at this same problem in an academic environment and have come to conclude that there is no way to reliably detect AI writing using only text. The reason for this is that no detector can take two identical inputs and classify one as synthetic (LLM) and one as organic (Human) and this situation can easily happen at the sentence or even paragraph level. Posed as a question, if a human writes a paragraph that happens to be exactly the same as a paragraph written by an LLM how do you classify that paragraph? There are a finite number of words and a finite way of combining them within an academic setting/field which means that we can't build a perfect classifier with just text. In practice, there are obvious tells and LLM-isms but these also change with time and each model has different tells so that even if a paper is full of LLM like writing we have no way to disambiguate between organic text that appears synthetic or synthetic text that appears organic.
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.
Re: How we measured AI writing across arXiv, and where the measurement breaks
#128Earlier quoted context omitted.
IMO, cognitive athrophy is the point. If humans become dependent on AI, those corporations will get enough leverage to change laws in their favor. For example, people don't want a huge data center in the middle of their city, but the corporations will say that the same people can't do anything without AI powered by this data center. That's why I think that making humans deficient and dependent on AI is the entire poi…
This is just conspiracy nonsense.
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Re: How we measured AI writing across arXiv, and where the measurement breaks
#129I've looked at this same problem in an academic environment and have come to conclude that there is no way to reliably detect AI writing using only text. The reason for this is that no detector can take two identical inputs and classify one as synthetic (LLM) and one as organic (Human) and this situation can easily happen at the sentence or even paragraph level. Posed as a question, if a human writes a paragraph that…
> There are a finite number of words and a finite way of combining them within an academic setting/field which means that we can't build a perfect classifier with just text.
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.
Re: How we measured AI writing across arXiv, and where the measurement breaks
#130Is it just me, or does this article itself read as AI generated?.. Ironically, I tried running the text of this article through their own classifier, and pretty much all of it was highlighted in red. I'm not sure what to make of this. Is this some kind of intentional irony?