Detecting LLM-Generated Texts with “Classical” Machine Learning

https://blog.lyc8503.net/en/post/llm-classifier/

by uneven9434 • 6 days ago

248 175 6 days ago

blog.lyc8503.net

cyanydeez3• 6 days ago

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.

pixl9710• 6 days ago

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

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

cyanydeez3• 6 days ago

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.

unfocso• 6 days ago

I had done the same for classifying and generating bookmarks of thousands of datasheets, along with a very naive yolo-based classificator (to detect pages made out of diagrams and pictures mostly).

Done with GLM-OCR, I had to watch text sloooowly crawl out of the llm and still have to live with hallucinations and the model not following the schema

Krssst4• 6 days ago

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!)

xiaoyu2006• 6 days ago

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

Krssst4• 6 days ago

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?

cygn2• 6 days ago

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

lcnhk42• 5 days ago

Hey this is really cool. Do you mind sharing a little bit about your approach? Is it similar SVM like algorithm or something else entirely?

aberoham• 6 days ago

I wonder about this technique vs simple SVM classifiers: https://x.com/rosmine/status/2056406399471558872?s=20

janalsncm2• 6 days ago

This article is about training a classifier to detect synthetic text.

The link you sent is for generating text which attempts to defeat those classifiers.

akersten• 6 days ago

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 tell-tale artifacts from today's generators that simply aren't emitted by "natural" paths to create them, and you can "detect AI" with high confidence (for now). Words, no, the signal is far too sparse and we are well into undetectable sophistication with today's models, let alone tomorrow's.

jgalt212• 6 days ago

It depends on how much text. For example, chardet often falls down on short strings, but 1K characters it nails it.

stymaar5• 6 days ago

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 to spot when reading AI slop, and that's not surprising that it's easy to spot automatically either. And I don't think that's going to change anytime soon, unless their incentives change.

(We can say exactly the same thing about man-made stuff optimized for a specific purpose, like stock photography, clickbait titles or industrial food: they aren't stereotypical because their creator lacks the skill to make them otherwise, they are like that because that's what works best).

empath75• 6 days ago

It does mean that this will have a drift problem if it's just trained on the idiosyncrasies of model fine tuning. That's fine! But it is something to be aware of.

ravenstine• 6 days ago

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 totally convincing output, but internet grifters don't need to go through that trouble. It's like how most phone, email, and social media scams come off as completely transparent to most of us, but that's the whole point; we're not the target audience of the scams. Readers looking for substance, nuance, and real opinions aren't going to notice if something with written by an LLM – unless there are some cliche punctuation tells.

BoredomIsFun• 6 days ago

especially base ones

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

AnthonyMouse5• 6 days ago

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 "maximize engagement".

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 classifier is being used.

zmjone2992• 6 days ago

i think one thing overlooked by this perspective is that many of a detectors adversaries are not that sophisticated. so despite this i think it is a useful thing to try to do. particularly when people are trying to do fraud which will often having to use abliterated models and generally trying to be as economical in their efforts

cyanydeez3• 6 days ago

Sure it is; we do it all the time, and then we modify each other's etc, etc; english we speak today was spoke yesterday waspake the same in yesteryears; we have no trouble dating english or other languages to a time.

A better argument is people themselves are just too influenced by reading that they'll sound like LLMs in a couple of years.

driverdan• 6 days ago

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 signs of AI writing and avoid everything it describes. https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing

WhitneyLand6• 6 days ago

"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.

Retric2• 6 days ago

Signal is easier to detect with more data to work with.

Largely AI generated books are a vastly different situation than a one paragraph homework assignment. But multiple rounds of homework assignments would change the accuracy.

yorwba3• 6 days ago

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."

onestay42• 6 days ago

Not all humans are in their right minds, unfortunately.

janalsncm2• 6 days ago

You definitely can rule out the general case a priori. If the problem were possible, for every text there would be a unique provenance label “human” or “ai”. But since humans and machines have both written many texts, it is not possible.

As an example, you could imagine a giant lookup table that deterministically mapped every text ever written to “human” or “AI”. You would very quickly run into situations where the labels conflict for the same piece of text.

The data is statistically inseparable which makes it impossible to classify from text alone.

sigmoid102• 6 days ago

That's like saying whether or not you're going to fall in love this year is just one bit of information, so you might be able to read it from astrology. Yeah, sure, it might happen for some people with a certain star sign. But across the population there is zero reason to believe that there is a) any significant correlation and b) enough data variation in to even distinguish classes of humans.

grayhatter• 6 days ago

Whether a text was written by a human or not is just a single bit of information

I doubt this models reality well at all. If I write the first paragraph, and AI writes the second; a float seems to model that better. If you choose to collapse a float into a bool, I don't think you can make useful conclusions based on that bit?

since even the shortest text contains more information than that.

I also don't think that's how information theory and bits of information works...

chungusamongus• 6 days ago

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.

Come on, that's circular reasoning.

jibal• 6 days ago

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.

WhitneyLand6• 6 days ago

"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

hgoel4• 6 days ago

So, if the decision from Pangram determined, on every assignment, if you would be expelled from university for plagiarism, would that be acceptable to you regardless of how you actually did the work?

If you would not be okay with that, what level of consequence would be acceptable for the output from this tool?

onecomment1• 6 days ago

This sounds like it was edited by an llm.

overgard• 6 days ago

I don't know, the thing about most text slop is how little effort goes into disguising it (for now, anyway). I'm sure anyone dedicated can go undetected, but it's the really low-effort stuff that's generally the problem. If you can catch some of it, that's something at least.

jaco6• 6 days ago

The best method is, as always, an anti-privacy method.

Simply track all citizens' writing patterns throughout their life, from cradle to grave, then diff with any given text's signature--you'll know if it was human written or not.

Better--opt in--install a "personal text signature" on your devices, sign things that you wrote yourself with it.

But I suppose that's just like the image provenance chips on cameras.

Either way father fascism is more with us than ever, praise him!

energy123• 6 days ago

pow(n,m) where n is alphabet size and m is number of characters is very dense.

TacticalCoder• 6 days ago

... but it's a bad fiction to perpetuate that any of this is anything more than tarot card reading

Most people's issue with AI-generated llmish however is not that it's AI-generated. It's its insufferable tone.

So if we get to a point where we have to read tea leaves (an image you seem to appreciate) to determine if it's llmish or not, we'll have won by then.

Really: it's that full-on asshole tone I (and many others) want to see disappear from blogs, comments, LinkedIn, etc.

dofm5• 6 days ago

My broad feeling is that if we generally independently identify the insufferable tone, and we absolutely can, so can a sufficiently trained machine-learning model.

It's an aside, but my biggest problem with trying to get up to date and learn about LLMs is how much of the documentation, blog writing, and tutorial material has obviously been written by no-one. It is just so much harder to read (and, like generative AI slop generally, curiously much harder to recall later).

bob10292• 6 days ago

With sufficient information you can derive a signal even in the presence of overwhelming noise. Assuming the noise is not perfectly correlated with the signal this is always possible.

Schemes like GPS, CDMA and DSSS are based upon this concept. GPS in particular is quite impressive in its ability to recover information that is received below the thermal noise floor.

Ferret7446• 6 days ago

There has to be a signal to detect it.

Take this sentence: Bob went to the store to buy milk.

Was that AI generated or not? There simply isn't a signal there. The problem isn't noise, the problem is, is there even a signal to begin with.

Sure, you might be able to recognize the quirks of a specific LLM just as you recognize the quirks of a particular person, but as the number of LLMs proliferate, then the signal turns into noise. (The signal isn't buried by noise, it becomes noise. The signal no longer has any discriminating power.)

itemize123• 6 days ago

obviously, a universal model doesn't exist since the signals are non-stationary but it's way better than what tarot reading

ekelsen• 6 days ago

See the discussion on https://news.ycombinator.com/item?id=48837460

You can absolutely still tell.

Jolter• 6 days ago

So you’re saying that the linked article’s findings are implausible? Is the article fake, then, in your opinion?

seanmcdirmid2• 6 days ago

If you have access to the detector, you can formulate a generative solution that avoids being flagged. Which gets me wondering why don’t model providers do that? There must be something about that that destroys semantic weights somehow.

lemagedurage5• 6 days ago

Why would sounding human be a goal rather than a byproduct of trying to communicate efficiently?

wzdd• 6 days ago

The article discusses a technique by which the author achieves high accuracy at detecting AI written text. Unless you have a problem with their experimental method, this is the opposite of tarot card reading.

we are well into undetectable sophistication with today's models

The article directly contradicts this, as do you, in your previous paragraph: "Sure you might be able to detect today's tells". The article is literally about a technique that detects today's tells.

Your comment is mostly expressing doubt that this technique will work reliably in the future, but it's framed as opposition to the article, which it's not: the article is about detecting today's AI-written text, at which it seems to be quite successful.

mschild• 6 days ago

It does achieve high accuracy but I think given the context when one wants to know this information, plagarism for research papers and college/highschool essays and work, it's unfortunately not good enough.

My neighbour is a teacher. She has a really good idea which of her students uses AI to do their homework but 80% accuracy is not good enough. She'd need to be able to prove it with certainty.

lelanthran2• 6 days ago

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.

This is simply untrue, and completely divorced from reality.

Tarot card readings have literally zero predictive success. Last I checked, LLM-detection had a +90% success.

dofm5• 6 days ago

Text is simply not information dense enough to be able to decode some arbitrary signal of provenance from it.

This does not sit well with personal experience and I wonder if it is just one of these questions of AI people being unaware of the level of skill that exists in domains they think have been automated.

It is of course possible that my tendency to spot LLM-written text has much to do with the way that it sounds like an averaged Californian college student to my British grammar-school-educated ears, as so many of the situations where I am encountering AI text are Brits using it without apparently realising they are giving themselves away.

But I know people who don't have particular technical skills in this sphere or a grammar-school background who also have an uncanny knack for pointing out LLM-written text.

Words, no, the signal is far too sparse and we are well into undetectable sophistication with today's models, let alone tomorrow's.

I especially don't think this is true. Will they be able to do it in the future? Maybe. Is it possible to prompt a current cloud LLM to write in a way that is obvious? Yeah. (IMO Gemma 4 writes less detectably than most of them!)

But my instinct is that someone with any facility for language is going to be better than chance at spotting LLM-written text once it is three or four paragraphs long. So I think it should be possible in principle to train machine learning systems to detect those patterns.

CalRobert3• 6 days ago

If you can train a system to detect these patterns, presumably you can train systems not to generate text which matches them?

I do struggle at times with thinking my own writing looks like AI. But I’m an average Californian who went to college half way between SF and LA…

saithound• 5 days ago

This does not sit well with personal experience and I wonder if it is just one of these questions of AI people being unaware of the level of skill that exists in domains they think have been automated.

I suspect the difficulty here lies more with your reading of the quoted sentence. British grammar school education, for all the years it devotes to the enterprise, does not always succeed in teaching reading comprehension.

You seem to be treating two rather different propositions as though they were one and the same. If text in general is not sufficiently information dense to support decoding some _arbitrary_ signal of provenance, that hardly establishes that no _specific_ passage can carry distinctive markers of provenance.

For example, you can recognize the unmistakable cadence of the California undergraduate. Impressive. Alas, even in your own example, when your British friends are "giving themselves away", you resort to an external signal, beyond the text, to determine provenance! That is, unless the text itself is claiming that its author is British (like the bots who claim they're John Horsetrader from Arkansas oblast).

When you have to decide whether a 2010s era SAT essay was from a SAT prep book author or an LLM prompted to write such an essay, you will struggle to distinguish one from the other. Not all texts have provenance signals. This is what it means for text to simply not be information dense enough to be able to decode some arbitrary signal of provenance from it.

XiphiasX• 6 days ago

Anything too “clever” and “snappy” = instaLLM

hasteg• 6 days ago

This is also how I pretty much filter LLM generated text in my head.

teeray2• 6 days ago

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.

dmurvihill• 6 days ago

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

teeray2• 6 days ago

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.

rayval• 6 days ago

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 item from June 2023 https://news.ycombinator.com/item?id=36215823

pixl9710• 6 days ago

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.

Krssst4• 6 days ago

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.

gleenn• 6 days ago

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 effective way to catch AI.

Retric2• 6 days ago

It’s an arms race where the AI companies are at an extreme disadvantage due to relative training costs.

arjie2• 6 days ago

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.

pixl9710• 6 days ago

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.

OtherShrezzing• 6 days ago

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.

t-writescode• 6 days ago

Could also be a problem of the form of P=NP. Validating might be very easy, but writing might be hard. Like the traveling salesman problem. It’s very easy to tell whether a specific path takes N units of time, but it’s hard to figure out if there’s any path, among all possible paths, that takes N units of time.

weitendorf• 6 days ago

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.

docheinestages• 6 days ago

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 details. That needs effort, with or without AI.

calebh• 6 days ago

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 to verify that text is written by a human.

visarga• 6 days ago

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?

warkdarrior2• 6 days ago

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.

Footprint0521• 6 days ago

As a verbal processor, I hate this. I use whisper v3 large all the time from voice memo dumps that are AI grammar corrected and this would flag when I copy paste it in :/

I wish there was a solution

throwaway219450• 6 days ago

Nevertheless this PoW is used by some conference venues now, in the case of a dispute against human authorship. NeurIPS does allow LLM use for "minor" polish like spelling, grammar, correcting non-native speakers. But you can't have the AI write the entire paper:

See https://blog.neurips.cc/2026/06/02/ai-generated-papers-in-th...

Unfortunately, given the volume of submissions that appear non-compliant, relying on author declarations is insufficient.

...

Authors whose submissions show significant AI involvement must provide an audit trail that clearly demonstrates that they complied with the policy. We expect that in future years this kind of audit trail will become a default.

The method uses Pangram, but that's somewhat arbitrary. The authors must provide the draft that was used to prompt the AI and show how the changes were made, which should be possible with a chat history. Where it doesn't work is if the authors claim they did not use AI, but otherwise it's quite hard to spoof. Edit history on its own isn't good enough because you could trivially have a program emulate keystrokes to type at a human speed into a document with tracked changes; the proof here accepts that AI was probably used and aims to determine to what extent.

I do think this goes back to if the text is high quality enough that humans don't notice, is that OK? Probably yes. What we don't want is poor quality writing from any source.

thatjoeoverthr• 6 days ago

Sufficiently advanced AI use is probably fine. The slop everyone complains about has certain tells specifically due to some combination of the following:

- The author is conducting some kind of hustle.

- The author doesn't bother editing.

- The author lacks the taste and awareness enough to see it looks.

- The author thinks you, the reader, lack taste and awareness.

- The author is using it as a kind of smoke bomb to get rid of you.

In such cases, nothing is done about the LLM's distinctive "voice". It dominates the text and it's easy to detect. It stands as a signifier of the above, even if it's otherwise not intrinsically a problem to use AI.

HDThoreaun• 6 days ago

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

Wowfunhappy3• 6 days ago

…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.

joebo• 6 days ago

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.

mr_mitm2• 6 days ago

How do you estimate your false negative rate?

pishpash• 6 days ago

What's likely to happen though, is human idiomatic writing will degrade to AI level and the two will converge. Just like nowadays it is harder to tell if some (human) non-native English speaker wrote some English text online whereas 20 years ago it was very obvious.

metalman• 6 days ago

there is not much point in detecting LLM generated text, in that humans are useing info from LLM's, but obfusicting it's origin, with there own garble, along with purely human garble, and almost(but not quite) human LLM product meaning that the threshold for rejecting "data" must be lowered, which personaly means a very very low tollerance for wierdness, except where it can yield imediate possitive cash flow for the rest I do my own research and verification thank you very much

mike_hock• 5 days ago

2 misplaced apostrophes, 8 spelling errors — definitely human output

40four2• 6 days ago

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 solve this problem.

IshKebab2• 6 days ago

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.

40four2• 5 days ago

I just mean no matter how hard anyone tries, I don’t see how useful these systems would be in practice. Sure they demonstrated a “working” system. Plenty companies sell products that “work” to one extent or another.

But how useful is it really to get a result of “This is 80% likely chance of being LLM generated”? Or 75%, or 95%? What if the text is a mix of human written text and LLM text? How would you even begin to test that?

I suppose a text that is half human half LLM would theoretically score in the 50% range, but do you see the problem? You can slap a confidence % score on a test run, but interpreting the results leads to a whole other can of worms.

Point is there are so many variables, and it’s not clear that the result from any of the systems is even valid or applicable to help you make a decision in a real life situation.

alok-g• 6 days ago

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

... if the assumption is LLMs are being optimized to evade such detection.

PS: I didn't read the actual article.

I think it is instruction-tuning that is having LLMs write differently from humans, and this is not being optimized away.

richard_chase• 6 days ago

Am I the only who largely enjoys the output of LLMs more than most stuff written by humans? I find myself coming back to old chats with ChatGPT frequently because the output is amazing.

therealdrag0• 6 days ago

I wouldn’t go that far… but it can be kinda like Wikipedia, clean and readable.

arjie2• 6 days ago

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.

pixl9710• 6 days ago

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.

Krssst4• 6 days ago

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.

cygn2• 6 days ago

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

connorboyle• 6 days ago

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!

jshmrsn• 6 days ago

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.

hgoel4• 6 days ago

I don't know if the Chinese text implies something different, but I think even in English it's pretty normal for people to claim they 'faked' their way through something without referring to fraud.

E.g. "I faked my way through the interview!" = "I did my best to respond to questions I did not feel fully prepared for, and managed to get through the interview"

woadwarrior01• 6 days ago

Small encoder-only transformers are excellent at classifying LLM-Generated Text. I built an on-device iOS app using a custom small encoder that achieves an AUROC of 99.81 on RAID-bench.

throaway54321• 6 days ago

I don’t think it actually matters (and it’s a losing strategy as others have noted).

This issue with AI generated stuff is that that it’s sometimes asymmetric: either the author worked very little to produce a lot of slop and now the reader(s) all have to do the heavy effort of reading it OR the author puts a little extra work in once and resolves all future readers’ burden.

If it was possible to boil down an artifact into a prompt + some resources that would be an interesting tool, or at least some way to tell if some artifact is “worth my time to read”

moxza• 6 days ago

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.

gdiamos• 6 days ago

as soon as you release a way of measuring it, you give LLMs a signal to optimize

maxspero• 6 days ago

Sounds promising, right? I spent some time trying [perplexity], but results were disappointing—plenty of false positives and false negatives, and no reasonable threshold could be set.

Perplexity was widely considered SOTA in 2022. One part of it is because everyone was evaluating on open models or closed models that were still close (i.e. GPT-2 vs. GPT-3.5). Today, the gap is so much wider between the models you can use to compute perplexity and the frontier models people actually use.

Also so many AI text detection papers used a strawman RoBERTa baseline that was very undertrained for the task.

The synthetic mirrors method for data generation used here is the same as what we use at Pangram. Good blog post, thank you for sharing!

sMarsIntruder• 6 days ago

Am I wrong or it doesn’t seem to detect the em-dashes as clear warning signal?

m00dy• 6 days ago

Reddit is doing this really good.

clickety_clack• 5 days ago

It looks like the text that this classifies might be Chinese, is that right? Do Chinese speakers have the same cultural aversion to AI-generated text? I’m wondering if there might be a different level of effort put into making text seem human-generated in English v. Chinese.

poisonfountain• 5 days ago

My theory is that the labs are RL'ing models to output easily classifiable text so they can avoid model collapse when training the models on data scraped from the web.

Of course, you can skew the distribution with some effort and generate text that avoids even the best classifiers out there (like Pangram), but even tech-savvy people aren't usually doing it (see the amount of AI-written posts that end up in HN and get tons of comments complaining about AI mannerisms), so I guess they're successfully avoiding like 99% of the slop using such classifiers.

I don't think it's in the interest of the labs to allow you to generate text that's indistinguishable from human prose. Especially since nobody would pay $1,000/mo just to generate text - but would do so for tasks like coding.

rtrgrd• 5 days ago

Oh man all of his Chinese humour is lost in translation T_T

amai• 5 days ago

Shouldn't there be a Kaggle contest for this?

Detecting LLM-Generated Texts with “Classical” Machine Learning

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