Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

Detecting LLM-Generated Texts with “Classical” Machine Learning

blog.lyc8503.net

Post reply on HN
Print view View original

101–110 of 184 posts

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

#101

Post by Krssst »

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.

We can measure false positive rate. The detector in the arricle is 85% accurate (not sure about false positives, but let's assume) which is too low to make conclusions, but enough when browsing the web and skipping reading likely-slop withiut accusing anyone.

If the false positive rate becomes <1% then it's better. The alternative is the world drowning under slop so I'd rather have imperfect detectors and have users aware they may fail in rare cases to avoid witch hunts. The general issue is that people only realize they're reading slop halfway through which is frustrating. If you know it from the start thanks to a detector and move on without commenting, no time waste, no frustration, less negativity towards LLM users.

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

#102

Post by Krssst »

pixl97 wrote: post #46
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.

If we get precise detectors and LLM posts don't get shown by social networks recommendation algorithms as a result, the chances of people starting to talk like LLMs get lower.

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

#103

Post by skinfaxi »

onestay42 wrote: post #81
yorwba wrote: post #64

Earlier quoted context omitted.

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…

Not all humans are in their right minds, unfortunately.

This is exactly the point I saw in a recent x post, that building anti-bot detection was incredibly difficult because some people exhibit bot like behavior.

Blizzard employee once told me anti-botting in WoW was extremely challenging due to the number of real people that acted identically to bots.

Every assumption was invalidated: - unbelievable # of consecutive hours played - consistently repetitive patterns of movement and clicks - farming patterns that aren’t considered fun (“why would anyone do that”) - solo, no external engagement - goes on for months

The problem with botting is many humans ARE bots

https://x.com/IceSolst/status/2076372992959959493

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

#104

Post by skinfaxi »

BoredomIsFun wrote: post #23
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…

> especially base ones Did you actually try them? I did.They generated even more "slopey" text than instruction-tuned ones.

But was it content indistinguishable from someone learning the language being used, for instance?

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

#105

Post by cygn »

Krssst wrote: post #4

The classifier does not seem so big, I wonder if something like it for English could be used in a browser extension to run against every single paragraph being displayed ? If the internet is going to drown in LLM text it would be nice to have tools to detect that automatically just like we have adblockers today to avoid wasting time on ads. (the article was a good read, thanks!)

I built a browser extension that does this, well for posts on twitter, hackernews, reddit etc. If you want it for all text, it would also be feasible. I use a quantized mini-LM model that runs very fast and classifies eg your whole twitter feed in a couple of seconds.

Check it out: https://slopsieve.com/extension

Accuracy is also much higher than this approach here. 0.9944 AUC, 0.966 acc@.5, 0.971 F1@.5

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

#106

Post by cygn »

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.

you can try my browser extension which does this for hackernews: https://slopsieve.com/extension

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

#107

Post by jibal »

yorwba wrote: post #64
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…

> 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.

This is word salad, a complete non sequitur.

> 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)

Er, that's obviously not true.

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

#108

Post by moxza »

The thing I find most encouraging is that the best AI detector is still humans. Don't write the Turing test off yet.

From what I understand, your approach is clever, it's like an accent detector. Known models tend toward a specific median approach. Humans have a much richer degree of randomness. Riffing on Anna Karenina... All models are alike in that they present predictable patterns. Humans inevitably write in unique ways.

I gave a lot of thought to the idea that humans will devolve to the median led by volume of AI interactions, but in the end, I think we're still interacting with each other when not at work/on machines, and the fact that we even have a genetic heritage is always going to differentiate us.

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

#109

Post by Der_Einzige »

WhitneyLand wrote: post #71
driverdan wrote: post #18

Earlier quoted context omitted.

There are two problems, false positives and changing the LLM's pattern. It's really easy to have a false positive and false positives can be very harmful if the person using the detector isn't aware of that risk. It's also very easy to change the pattern of LLM output. You can provide basic prompting that will significantly change the structure of the output. For example, having it utilize the Wikipedia article on si…

"It's really easy to have a false positive" Not really. The false positives for the SOTA detector are very very low. "It's also very easy to change the pattern of LLM output." Not in a way that can reliably avoid detection. The problem is the patterns are baked into the distribution itself. It's smoothed over, so it becomes difficult to prompt your way out of that.

Wrong. Effective sampling (I.e high temperature like temp 10) with the corresponding sampler stack that enables this coherently destroys all attempts to detect it. There are many more ways like this involving manipulating the logprobs

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

#110

Post by weitendorf »

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…

Because model providers are not optimizing for being indistinguishable from human text, and in fact, there is more value/demand in modeling a different distribution (ie an “agent” capable of producing vast amounts of concrete procedural/planning text interspersed) than there is in modeling the way humans write (ie GPT3).

Also you have to keep in mind that most AI companies are in fact trying to create and offer legitimate products and services to customers doing actually-useful work. They’re not trying to help fly by night hustlers scam people out of crypto or run spam campaigns, and in fact often voluntarily watermark to prevent misuse of their products.

You could argue that’s “just to avoid bad PR” and maybe you’re right, but that’s just another way of saying that it’s more profitable to prioritize other use cases than the deepfake/spam market. Spammers and fraudsters are shitty customers and a major brand risk.

Post reply on HN