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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

#41

Post by pixl97 »

OneManyNone wrote: post #32
AnthonyMouse wrote: post #30

Earlier quoted context omitted.

> But commercial chat models are specifically tuned in a way that maximizes user engagement. It's that specific tuning that is very easy to spot when reading AI slop, and that's not surprising that it's easy to spot automatically either. There are two problems with this. The first is that it would still misclassify human-authored text written under the same incentive, and most people have various incentives to "maxim…

All of that may be true, but pangram currently has a false positive rate of about 1 in 10000, and this has been tested by feeding in thousands of texts written before 2020. That may not last if AI companies start trying to build models that fool it, but for the time being at least, modern models do have strong tells.

>and this has been tested by feeding in thousands of texts written before 2020.

And these text didn't train the model in the first place? I just want to ensure clarity on that.

>pangram currently has a false positive rate of about 1 in 10000

Says Panagram.

The problem with just looking at old text is language is a living thing. Say for example I make up the world 'oklambroahaha' right today. Both humans and AI pick up that word and start using it. Now lets say the model says that anything that uses oklambroahaha is 100% AI, you can't just point and say, "well my detection AI is correct on things 20 years old, so it's right skibbidy toilet 6/7".

There is a ton of evidence that use of AI changes the way we speak and write, so it will just turn these AI detectors into bullshit generating classifiers.

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

#42

Post by vidarh »

AnthonyMouse wrote: post #37
OneManyNone wrote: post #32

Earlier quoted context omitted.

All of that may be true, but pangram currently has a false positive rate of about 1 in 10000, and this has been tested by feeding in thousands of texts written before 2020. That may not last if AI companies start trying to build models that fool it, but for the time being at least, modern models do have strong tells.

You can get an arbitrarily low false positive rate by sacrificing against false negatives. It's trivial to make it zero, just classify everything as human-generated. Meanwhile a false negative rate of even 1% is a pretty big problem since someone can easily use LLMs to generate 100x the volume of text and then use whichever ones make it through the classifier. And that's before anyone even tries to get the LLM to gen…

You don't really need a style model - current models are very good at doing "style transfer" of a model text onto whatever it has written if you just have it do it chunk by chunk. It takes more to prevent it from being detectable by good detectors, but it does remove a lot of the worst tells.

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

#43

Post by pixl97 »

ravenstine wrote: post #16
stymaar wrote: post #11

Earlier quoted context omitted.

> but it's a bad fiction to perpetuate that any of this is anything more than tarot card reading. Hard disagree. LLMs (especially base ones, that only received pre-training) can produce output that is undistinguishable from human writing (because that's what they were trained to do). But commercial chat models are specifically tuned in a way that maximizes user engagement. It's that specific tuning that is very easy…

They're also designed to not offend anybody, so their output tends to be very bland even compared to the most milquetoast of human beings. I was only surprised once when ChatGPT responded with an enthusiastic "hell yes" seemingly organically, but 99.9% of the time these AI services clearly are instructed and trained to provide flavorless word vomit. I don't think there's a technical reason why an LLM couldn't produce…

When DANmode bypasses were a common thing the LLMs would drift significantly far from corporate speak.

But that's the point of corporate speak, you tend not to say thing that may offend your clients and deprive the company of future revenue. Of course there are some companies that make their living being 'counter-culture' and saying what they want, but they are a small percentage of all revenue.

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

#44

Post by AnthonyMouse »

stymaar wrote: post #38
AnthonyMouse wrote: post #30

Earlier quoted context omitted.

> But commercial chat models are specifically tuned in a way that maximizes user engagement. It's that specific tuning that is very easy to spot when reading AI slop, and that's not surprising that it's easy to spot automatically either. There are two problems with this. The first is that it would still misclassify human-authored text written under the same incentive, and most people have various incentives to "maxim…

> 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…

> The thing is, humans are significantly worse at maximizing numerical goals than computers.

I'm not sure this is even the right premise.

Existing LLMs try to maximize engagement, and they often write in a particular style that has tells, but these two things are not necessarily related. Over-using em-dash or whatever isn't the thing that maximizes engagement.

So the two problems really are, what happens to the actual humans whose writing style is a close match for what a given generation of LLMs output? And, what stops LLMs from using a different style when someone wants to fool the classifier?

> People posting slop content don't care if pangram or I flag their slop with certainty, they are using the easiest option, which is commercial chat models.

They don't care as long as the consequences of identifying it are immaterial, but in that case what's the point of classifying it? Whereas if they need to fool the classifier some threshold percentage of the time in order for enough of their spam to get through, they're going to care.

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

#45

Post by pixl97 »

Krssst wrote: post #7
xiaoyu2006 wrote: post #5

Earlier quoted context omitted.

I assume different models will have different distribution, so it has to be kept updated?

The article mentions that AI texts are often caught by multiple models, so hopefully text from newer LLMs could still be caught without updating the model?

You know what GAN is, right?

In training all you have to do is take their model as the adversary and then it's useless.

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

#46

Post by pixl97 »

arjie wrote: post #36

Neat. I will implement something like this for myself. I just need to reduce the spam a little. Imperfection is okay for a social network context like HN.

It will work for a bit, but as people start speaking more like LLMs and LLMs start training using said classifiers as a GAN, it will become useless.

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

#47

Post by pixl97 »

rayval wrote: post #35
teeray wrote: post #12

The problems are simply too great if an LLM detector has any false positives at all. Imagine how soul-crushing writing an entire dissertation by hand and having it rejected because some “good enough” LLM detector decides you write too much like an AI.

As I recall, a few years ago (in the era of first generation LLMs), a professor in Texas used an anti-plagiarism tool that flagged more than one-third of the class using AI in an exam, and used that finding to give them a failing grade. If memory serves, one student objected strenously and ran the professor's own work (published 10 years earlier) into the same tool and it flagged that work as AI-generated. EDIT: HN i…

Exactly. The more corporate and proper you tend to speak, the more likely it's to classify you as an LLM. It's like the classifiers want us to talk like trash at their current rate. This seems to be really problematic for ESL speakers/typers that may have been trained on a smaller, more proper subset of the language.

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

#48

Post by pixl97 »

cyanydeez wrote: post #2

today, sure. Tomorrow, the LLMs will be training the humans thought patterns that will directly start skewing their natural writing. Generation alpha is going to have a lot of trouble if we keep perpetuating the myth that you can really interpret text in an ongoing fashion.

I think you're about a year late for this revolation.

https://www.washingtonpost.com/opinions/2025/08/20/chatgpt-c...

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

#49

Post by theoreticalmal »

visarga wrote: post #34
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…

That human might have used AI. You can never know. Hand fixed AI output, human just polished the corners? Light rewording of a full text written by hand, because the author is not confident in their writing? Actual human text, but after researching with AI?

Exactly. Detecting AI writing is an arms race that can only end with detection coming in second place.

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

#50

Post by warkdarrior »

calebh wrote: post #31
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…

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.
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