"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

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."

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

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.

> 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).

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

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.

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

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.

…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 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.

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.

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

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.

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

> 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.

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.

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.

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.

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

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.

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

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

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

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

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

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