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Detecting LLM-Generated Texts with “Classical” Machine Learning

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Re: Detecting LLM-Generated Texts with “Classical” Machine Learning

#61

Post by pixl97 »

cyanydeez wrote: post #54
pixl97 wrote: post #48

Earlier quoted context omitted.

I think you're about a year late for this revolation. https://www.washingtonpost.com/opinions/2025/08/20/chatgpt-c...

I'm not late if people constantly put effort into finding LLM text, or every other comment on hacker news is either about something being LLM generator.

After seeing comments on hacker news attempt to call an article from 2015 as generated by an LLM, I have very little faith in commenters having any ability in actually detecting AI written text.

And that's just one particularly egregious case I remember. Posters that are technical writers or use English properly get called bots quite commonly when their post history shows a writing style going back over a decade.

But now that LLMs are causing a language drift in English users our filters of "that's an LLM" will become even more useless.

Re: Detecting LLM-Generated Texts with “Classical” Machine Learning

#62

Post by bulder »

warkdarrior wrote: post #50
calebh wrote: post #31

Earlier quoted context omitted.

The easiest way is to keep track of the text's edit history, keeping a block of edits over time and having them signed by a timestamp authority. The final edit history can then be inspected by some external authority, then signed if the edit history looks human. I have a blog post from 2023 on this topic: https://helbl.ing/Written-Proof-of-Work/ For Google Doc users, you can already inspect the edit history over time…

I am working on a browser extension to help with that. Basically it interposes on any text field and canvas and if user pastes a large amount of text (copied form example from a chat bot), the extension will "replay" that text at normal, human-editing pace, and introduce typos that are fixed through later edits.

Any specific reason as to why you'd want to make that, outside of intentionally enabling fraud?

Re: Detecting LLM-Generated Texts with “Classical” Machine Learning

#63

Post by Wowfunhappy »

docheinestages wrote: post #24

I think figuring out if a text is AI-made is a losing battle. What could work is gauging how much effort went into writing the text, regardless of who the author might be. What's easy today is generating mountains of text that are extremely hard to read. What requires effort is knowing how to engage the reader, how to keep out extraneous information, and how to keep the text as short as possible without losing detail…

…All I know is that sometimes I will read e.g. a HN submission, and it becomes pretty obvious partway through that the article was AI generated.

If I can do it, an algorithm should be able to do it. Maybe in the future the models will get so good that it is literally impossible to differentiate human vs computer authorship, but that’s obviously not the case today.

Re: Detecting LLM-Generated Texts with “Classical” Machine Learning

#64

Post by yorwba »

akersten wrote: post #8

Text is simply not information dense enough to be able to decode some arbitrary signal of provenance from it. Sure you might be able to detect today's tells (particular sentence structures preferred by Claude, phrases, etc) to get you some arbitrary chance percentage it was machine generated, but it's a bad fiction to perpetuate that any of this is anything more than tarot card reading. Images, absolutely, there are…

Whether a text was written by a human or not is just a single bit of information. So you can't rule out its detectability a priori, since even the shortest text contains more information than that.

As long as LLMs are used to write texts humans wouldn't want to write if they could help it (that's why they're getting an LLM to do it, after all), they'll remain detectable. Even if the reasoning might end up equivalent to "This looks like spam; no human in their right mind would write this spam by hand if they could get an LLM to write it, therefore it's most likely written by an LLM."

Re: Detecting LLM-Generated Texts with “Classical” Machine Learning

#65

Post by connorboyle »

> Eventually, I faked my way through the thesis, and life moved on.

This is a very startling admission! I checked the Chinese (original?) version of the post, and saw the author uses the word "糊弄" (in the place of "faked"); I'm not a native speaker but I think this may come across more as a self-effacing comment on the low quality and/or effort behind their thesis, whereas the English version implies fraud. May be wise to change this!

Re: Detecting LLM-Generated Texts with “Classical” Machine Learning

#66

Post by stymaar »

pixl97 wrote: post #52
stymaar wrote: post #38

Earlier quoted context omitted.

> The first is that it would still misclassify human-authored text written under the same incentive, and most people have various incentives to "maximize engagement". The thing is, humans are significantly worse at maximizing numerical goals than computers. > And the second is that then people would just make other models that are tuned for defeating that sort of classifier, which would be used whenever the classifie…

I mean, back when I was spam filtering setting up a simple Bayesian classifier was easy. Train it on your spam and ham and it worked damned good. "Mission Accomplished".... until it wasn't. Spam rates started climbing and it started getting harder than ever to filter them. There is always an incentive to get spam to bypass filters, so as your filters increase in accuracy, those attempting to pass said filters adjust…

I don't think it's a very good remark, as there's significantly less email spam than 20 years ago.

Another example is ad-blocker-blocker. There was a little bit of an arm race between ad blockers and advertisers in the middle of the 2010s, but it didn't last long. Advertisers mostly just decided not to care about ad-blockers.

Re: Detecting LLM-Generated Texts with “Classical” Machine Learning

#67

Post by WhitneyLand »

akersten wrote: post #8

Text is simply not information dense enough to be able to decode some arbitrary signal of provenance from it. Sure you might be able to detect today's tells (particular sentence structures preferred by Claude, phrases, etc) to get you some arbitrary chance percentage it was machine generated, but it's a bad fiction to perpetuate that any of this is anything more than tarot card reading. Images, absolutely, there are…

"Text is simply not information dense enough to be able to decode some arbitrary signal of provenance from it...it's a bad fiction to perpetuate that any of this is anything more than tarot card reading."

Not true at all. Pangram is highly effective and has a very low false positive rate.

The post here is impressive for a small project, it looks like they independently thought of one of the core ideas Pangram uses of creating twins to compare.

You can see how it works here: https://arxiv.org/pdf/2402.14873

Re: Detecting LLM-Generated Texts with “Classical” Machine Learning

#68

Post by jshmrsn »

connorboyle wrote: post #65

> Eventually, I faked my way through the thesis, and life moved on. This is a very startling admission! I checked the Chinese (original?) version of the post, and saw the author uses the word "糊弄" (in the place of "faked"); I'm not a native speaker but I think this may come across more as a self-effacing comment on the low quality and/or effort behind their thesis, whereas the English version implies fraud. May be wi…

Well cheated would definitely imply fraud. “Faking it” as in “fake it till you make it” is more like pretending you know about a topic until you learn enough on the job to participate competently.

Re: Detecting LLM-Generated Texts with “Classical” Machine Learning

#70

Post by joebo »

Wowfunhappy wrote: post #63
docheinestages wrote: post #24

I think figuring out if a text is AI-made is a losing battle. What could work is gauging how much effort went into writing the text, regardless of who the author might be. What's easy today is generating mountains of text that are extremely hard to read. What requires effort is knowing how to engage the reader, how to keep out extraneous information, and how to keep the text as short as possible without losing detail…

…All I know is that sometimes I will read e.g. a HN submission, and it becomes pretty obvious partway through that the article was AI generated. If I can do it, an algorithm should be able to do it. Maybe in the future the models will get so good that it is literally impossible to differentiate human vs computer authorship, but that’s obviously not the case today.

I've noticed there seems to be a default style that is easier to detect. I've noticed it harder to detect when asking an LLM to use a different style (more conversational, avoid sounding like an AI, don't use emdashes, etc). I wonder if that's what you're picking up too - the instances where people make no effort to change the style of the output.
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