Detecting LLM-Generated Texts with “Classical” Machine Learning • Hacker News Board

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

#51

Post by AnthonyMouse » Thu, Jul 16, 2026, 7:35 PM UTC

vidarh wrote: post #42

AnthonyMouse wrote: post #37

Earlier quoted context omitted.

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.

The point being that you wouldn't need the developers of the most popular models to themselves be trying to fool classifiers because their output could be run through an independent special purpose one designed to remove the tells the classifier is looking for, and the special purpose one wouldn't need to be made by anyone with the resources to create a good general-purpose model since it only has to do that one thing.

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

#52

Post by pixl97 » Thu, Jul 16, 2026, 7:36 PM UTC

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…

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 their behaviors.

Spammers/cheaters/whateverers will at least just use a second pass filter that uses one of these 'ai scoring' systems to beat said AI scoring systems. So while it's worthwhile to do it at this moment, this window will rapidly close.

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

#53

Post by OtherShrezzing » Thu, Jul 16, 2026, 7:36 PM UTC

gleenn wrote: post #14

I think the fundamental problem is that training current SOTA AI models is very expensive. If a simple "classical" model can detect them, presumably at much lower algorithmic cost, then why wouldn't the model trainers use these same tools to feed back into their models to improve them at low cost to make them better? It's an arms race. Any cheap pattern can and presumably will be used to retrain if it becomes and eff…

In part because model vendors specifically prefer when people think that lots of content is produced by their model. The more Claude-like writing appears on the internet, the more signal there is to investors that people are using Claude for a greater number tasks.

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

#54

Post by cyanydeez » Thu, Jul 16, 2026, 7:38 PM UTC

pixl97 wrote: post #48

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...

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.

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

#55

Post by teeray » Thu, Jul 16, 2026, 7:41 PM UTC

dmurvihill wrote: post #27

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.

It depends on the application. Dissertation? Hell naw. Blog post? Absolutely, run it through that thing.

The problem is that ed-tech is absolutely ravenous for an LLM detector and would rather use snake oil than accept that it might not be possible.

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

#56

Post by HDThoreaun » Thu, Jul 16, 2026, 7:42 PM UTC

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…

There cant be a way / except of course if you pay / mind to my syllables

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

#57

Post by nullsanity » Thu, Jul 16, 2026, 7:43 PM UTC

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…

[dead]

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

#58

Post by pixl97 » Thu, Jul 16, 2026, 7:43 PM UTC

arjie wrote: post #28

gleenn wrote: post #14

I think the fundamental problem is that training current SOTA AI models is very expensive. If a simple "classical" model can detect them, presumably at much lower algorithmic cost, then why wouldn't the model trainers use these same tools to feed back into their models to improve them at low cost to make them better? It's an arms race. Any cheap pattern can and presumably will be used to retrain if it becomes and eff…

It’s simply not a priority. The labs can do many things. Making text non-LLM is not really that useful. Analogous to Facebook not picking up the obvious $20 bill in front of them. It’s because they’ve got $100 bills at their feet they’re picking up.

Not a priority currently. Selling services to spammers... I mean marketers is still big money and eventually someone will pick it up. If training costs ever drop, then it's one of the first things that will happen.

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

#59

Post by IshKebab » Thu, Jul 16, 2026, 7:43 PM UTC

40four wrote: post #26

I could be wrong, but I just don’t see how trying to “detect” LLM generated texts is ever going to work. The only thing that makes any sense if you truly want to have confidence a human wrote it is some type of “proof of work“ system. I think there’s a lot of interesting ways to approach the proof of work problem with different pros and cons, but that is where our energy should be focused if we seriously want to solv…

> I just don’t see how trying to “detect” LLM generated texts is ever going to work

He literally demonstrated a working system in this post. Do you mean you'll never get to 100% accuracy? Clearly, but you don't need that.

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

#60

Post by stymaar » Thu, Jul 16, 2026, 7:46 PM UTC

AnthonyMouse wrote: post #44

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…

> 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 actua…

> Over-using em-dash or whatever isn't the thing that maximizes engagement.

It's the thing that minimizes the loss during the RLHF phase, and the RLHF phase is the one that's aimed at maximizing engagement (it's literally trained on that).

> what happens to the actual humans whose writing style is a close match for what a given generation of LLMs output?

If a human, for instance because its writing gets polluted by reading too much AI slop, matches the style of an LLM closer than a certain threshold, then his own writing is going to be flagged as well. Whether it's an actual problem or merely a theoretical one is an open question. (unlike OpenAI and Anthropic, humans writers do have an incentive to avoid being flagged as AI).

> And, what stops LLMs from using a different style when someone wants to fool the classifier?

In theory: nothing. In practice if you fine-tune your own model: nothing. In practice with commercial models: the interests of the model making company.

> And, what stops LLMs from using a different style when someone wants to fool the classifier?

Websites have pretty much stopped using ad-blocker-blockers, it seems that it's not a fight worth fighting for them. Does that mean that ad-blockers are useless?

Most people don't even care about ads, I don't think they care about slop either, that's why there's slop posts and obnoxious websites that are unreadable without an ad blocker. A slop blocker used by 10-20% of the internet users wouldn't change the calculation more than ad blockers did.