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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

#141

Post by duskdozer »

Footprint0521 wrote: post #93
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…

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

Use it to transcribe directly without correcting grammar or formatting.

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

#142

Post by seanmcdirmid »

lemagedurage wrote: post #129
seanmcdirmid wrote: post #127

Earlier quoted context omitted.

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.

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

That is manager/executive/manager speak, real people don’t speak like that (unless they are in the aforementioned roles).

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

#143

Post by lemagedurage »

seanmcdirmid wrote: post #142
lemagedurage wrote: post #129

Earlier quoted context omitted.

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

That is manager/executive/manager speak, real people don’t speak like that (unless they are in the aforementioned roles).

I just feel like, from the POV of AI companies, that reducing the amount of em dashes they use to "blend in" more and talk more human-like, for the sake of being less detectable, wouldn't be a big priority.

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

#144

Post by lemagedurage »

sigmoid10 wrote: post #136
lemagedurage wrote: post #130

Earlier quoted context omitted.

The 80% accuracy from the article would be one reason to believe there's significant correlation, no?

I could probably find quite a lot of people who will tell you astrology is 80+% correct for them. Would you believe them or wait for an independent analysis? There are other AI "detector" systems out there that claim 99% accuracy. But independent research always found that they are actually garbage once used on real data. It's all in how you pick your tests. It's also funny to see how people on places like HN will ea…

Well I don't think the position of planets when you're born has a large correlation to how your life will go.

On the other hand, how an AI writes will have a big correlation to whether the written text would likely be written by an AI.

The latter is more of a direct relationship.

Maybe A and B are not correlated, and Y and Z are? What pattern are people falling for here?

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

#145

Post by yorwba »

sigmoid10 wrote: post #90
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…

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.

Indeed you cannot rule out astrology on information-density grounds. Astrology involves quite a lot of information, the problem is that it's mostly unrelated to the outcomes of interest. To get back to the information-density of text, "I love you" doesn't contain a lot of information, but it does contain the one bit you care about, because someone who loves you is more likely to say it than someone who doesn't.

So if you want to determine whether something was written by a human or by AI, to do better than chance it's enough for there to be a difference in the probabilites of a human writing it and AI writing it, respectively. Whether the resulting accuracy is good enough for a particular use case is another matter. 99% is pretty good odds for love and pretty bad odds for "am I going to survive today?" Hopefully there won't be a death penalty for posting AI-generated content.

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

#146

Post by cws_ai_buddy »

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!)

[flagged]

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

#148

Post by bob1029 »

Ferret7446 wrote: post #113
bob1029 wrote: post #98

Earlier quoted context omitted.

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.

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 turn…

Statistical power comes from having many samples. I agree that having just one sample doesn't take you very far.

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

#149

Post by yorwba »

mschild wrote: post #132
wzdd wrote: post #128

Earlier quoted context omitted.

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 l…

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.

The context in which the author wants to know this information is that they enjoy reading fiction, but not low-effort AI fiction, so they built a tool to filter out some of the low-effort AI fiction. Not every application is such a high-stakes affair that anything less than perfect isn't good enough.

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

#150

Post by throwaway219450 »

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…

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.

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