That doesn't mean that it can't be distguishable by some other means.
LLMs clearly pass the single sentence test. If you generate far more text than their window, I'm pretty sure they'd clearly fail as they start getting repetitive or losing track of what they've written. In between, it varies depending on how much text you get to look at. A single paragraph is pretty darned hard. A full essay starts becoming something I'm more confident in my assessment.
It's also worth reminding people that LLMs are more than just "ChatGPT in its standard form". As a human trying to do bot detection sometimes, I've noticed some tells in ChatGPT's "standard voice" which almost everyone is still using, but once people graduate from "Write a blog post about $TOPIC related to $LANGUAGE" to "Write a blog post about $TOPIC related to $LANGUAGE in the style of Ernest Hemmingway" in their prompts it's going to become very difficult to tell by style alone.
"All I have to do is modify my virus until the anti-virus doesn't detect it."
>It should only attempt a distinct style if prompted to.
There is no such thing as an indistinct style. Any particular style it could have would be made distinct by it being the style ChatGPT chooses to answer in.
The answers that ChatGPT gives are usually written in a style combining somewhat dry academic prose and the type of writing you might find in a Public Relations statement. ChatGPT sounds very confident in the responses it generates to the queries of users, even if the actual content of the information is quite doubtful. With some attention to detail I believe that it is quite possible for humans to emulate that style, further I believe that the style was designed by the creators of ChatGPT to make the output of the machine learning algorithm seem more trustworthy.
The point being that it's already possible to change ChatGPT's tone significantly. Think of how many people have done "Write a poem but as if <blah famous person> wrote it". The idea that ChatGPT could be reliably detected is kind of silly. It's an interesting problem but not one I'd feel comfortable publishing a tool to solve.
Moreover, the way to deal with AI in this context is not like the way to deal with plagiarism; do not try to detect AI and punish its use.
Instead, assign it's use, and have the students critique the output and find the errors. This both builds skills in using a new technology, and more critically, builds the essential skills of vigilance for errors, and deeper understanding of the material — really helping students strengthen their BS detectors, a critical life skill.
I listened to a podcast with Scott Aaronson that I'd highly recommend [0]. He's a theoretical computer scientist but he was recruited by OpenAI to work on AI safety. He has a very practical view on the matter and is focusing his efforts on leveraging the probabilistic nature of LLMs to provide a digital undetectable watermark. So it nudges certain words to be paired together slightly more than random and you can mathematically derive with some level of certainty whether an output or even a section of an output was generated by the LLM. It's really clever and apparently he has a working prototype in development.
Some work arounds he hasn't figured out yet is asking for an output in language X and then translating it into language Y. But those may still be eventually figured out.
I think watermarking would be a big step forward to practical AI safety and ideally this method would be adopted by all major LLMs.
That part starts around 1 hour 25 min in.
> Scott Aaronson: Exactly. In fact, we have a pseudorandom function that maps the N-gram to, let’s say, a real number from zero to one. Let’s say we call that real number ri for each possible choice i of the next token. And then let’s say that GPT has told us that the ith token should be chosen with probability pi.
https://axrp.net/episode/2023/04/11/episode-20-reform-ai-ali...
I personally would want to live in Aaronson's world, and not the world where a centralized authority controls the definition of reality.
However, as soon a detection tool becomes publicly available (or even just the knowledge that watermarking has been implemented internally), a simple enough garbling LLM would pop up that would only need to be smart enough to change words and phrasing here and there.
Of course these garbling LLMs could have a watermark of their own... So it might turn out to be a kind of cat-and-mouse game but with strong bias towards the mouse, as FOSS versions of garblers would be created or people would actually do some work manually, and make the changes by hand.
The water marking techniques also can not work after some level of sophisticated rewriting. There simply will be no data encoded in the probabilities of the words.
You don't see the writing on the wall? OK, here is big hint: it might make a huge difference from a legal perspective whether some "photo" showing child sexual abuse (CSA) was generated using a camera and a real, physical child, or by some AI image generator.
A world where I can't tell if something is made by human or by machine is a world that has been drained of something important to me. It would reduce the appeal of all art for me and render the world a bit less meaningful.
The upshot of which is, the useful writing assignments I used to give as homework will either have to be done in class (wasting valuable class time) or given up altogether (wasting valuable learning experiences).
And as a teacher who really WANTS them to learn and to get that feeling, "Hey, I can actually do this!", it's depressing to think of the one who do cheat themselves. Oh well...
I think you could pick up right quick on who understood what they wrote, and who didn't.
They once said I should join Line then we can all talk, then I asked if it's possible to talk in groups of 200+ and their eyes got really big.
If your students want to betray themselves of the possible learning opportunities of attempting to formulate the sentences by themselves in English, it is their problem.
The same holds in mathematics (degree course): of course, in the first semesters, you can use a computer algebra system like Maple or Mathematica for computing the integrals on your exercise sheets, but you will betray yourself of the practice of computing integrals that these exercise sheets are supposed to teach you.
The "detector" has extremely little information and the only somewhat reasonable criteria are things like style, where ChatGPT certainly has a particular, but by no means unique writing style. And as it gets better it will (by definition) be better at writing in more varied styles.