> 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.

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OneManyNone 3 hours ago | parent | next [-]

All of that may be true, but pangram currently has a false positive rate of about 1 in 10000, and this has been tested by feeding in thousands of texts written before 2020.

That may not last if AI companies start trying to build models that fool it, but for the time being at least, modern models do have strong tells.

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pixl97 2 hours ago | parent | next [-]

>and this has been tested by feeding in thousands of texts written before 2020.

And these text didn't train the model in the first place? I just want to ensure clarity on that.

>pangram currently has a false positive rate of about 1 in 10000

Says Panagram.

The problem with just looking at old text is language is a living thing. Say for example I make up the world 'oklambroahaha' right today. Both humans and AI pick up that word and start using it. Now lets say the model says that anything that uses oklambroahaha is 100% AI, you can't just point and say, "well my detection AI is correct on things 20 years old, so it's right skibbidy toilet 6/7".

There is a ton of evidence that use of AI changes the way we speak and write, so it will just turn these AI detectors into bullshit generating classifiers.

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AnthonyMouse 3 hours ago | parent | prev | next [-]

You can get an arbitrarily low false positive rate by sacrificing against false negatives. It's trivial to make it zero, just classify everything as human-generated. Meanwhile a false negative rate of even 1% is a pretty big problem since someone can easily use LLMs to generate 100x the volume of text and then use whichever ones make it through the classifier.

And that's before anyone even tries to get the LLM to generate a different style of text. Or for that matter creates a "style model" that rephrases text.

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vidarh 2 hours ago | parent [-]

You don't really need a style model - current models are very good at doing "style transfer" of a model text onto whatever it has written if you just have it do it chunk by chunk. It takes more to prevent it from being detectable by good detectors, but it does remove a lot of the worst tells.

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AnthonyMouse 2 hours ago | parent [-]

The point being that you wouldn't need the developers of the most popular models to themselves be trying to fool classifiers because their output could be run through an independent special purpose one designed to remove the tells the classifier is looking for, and the special purpose one wouldn't need to be made by anyone with the resources to create a good general-purpose model since it only has to do that one thing.

	▲	vidarh 27 minutes ago \| parent [-]
		My point is that you don't need a special purpose one to achieve this.

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vidarh 2 hours ago | parent | prev [-]

Pangram won't know how much AI written text they fail to detect, though, and detectors is a great tool to adjust methods of generating less AI-sounding text.

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stymaar 2 hours ago | parent | prev [-]

> 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".

The thing is, humans are significantly worse at maximizing numerical goals than computers.

> 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.

Anyone can already do that right now, just grab unsloth studio and fine-tune your local Gemma, but nobody cares. People posting slop content don't care if pangram or I flag their slop with certainty, they are using the easiest option, which is commercial chat models. And given this segment of user doesn't care, the provider have zero incentive to provide a dedicated stealth model for that purpose.

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AnthonyMouse 2 hours ago | parent | next [-]

> The thing is, humans are significantly worse at maximizing numerical goals than computers.

I'm not sure this is even the right premise.

Existing LLMs try to maximize engagement, and they often write in a particular style that has tells, but these two things are not necessarily related. Over-using em-dash or whatever isn't the thing that maximizes engagement.

So the two problems really are, what happens to the actual humans whose writing style is a close match for what a given generation of LLMs output? And, what stops LLMs from using a different style when someone wants to fool the classifier?

> People posting slop content don't care if pangram or I flag their slop with certainty, they are using the easiest option, which is commercial chat models.

They don't care as long as the consequences of identifying it are immaterial, but in that case what's the point of classifying it? Whereas if they need to fool the classifier some threshold percentage of the time in order for enough of their spam to get through, they're going to care.

	▲	stymaar 2 hours ago \| parent [-]
		> Over-using em-dash or whatever isn't the thing that maximizes engagement. It's the thing that minimizes the loss during the RLHF phase, and the RLHF phase is the one that's aimed at maximizing engagement (it's literally trained on that). > what happens to the actual humans whose writing style is a close match for what a given generation of LLMs output? If a human, for instance because its writing gets polluted by reading too much AI slop, matches the style of an LLM closer than a certain threshold, then his own writing is going to be flagged as well. Whether it's an actual problem or merely a theoretical one is an open question. (unlike OpenAI and Anthropic, humans writers do have an incentive to avoid being flagged as AI). > And, what stops LLMs from using a different style when someone wants to fool the classifier? In theory: nothing. In practice if you fine-tune your own model: nothing. In practice with commercial models: the interests of the model making company. > And, what stops LLMs from using a different style when someone wants to fool the classifier? Websites have pretty much stopped using ad-blocker-blockers, it seems that it's not a fight worth fighting for them. Does that mean that ad-blockers are useless? Most people don't even care about ads, I don't think they care about slop either, that's why there's slop posts and obnoxious websites that are unreadable without an ad blocker. A slop blocker used by 10-20% of the internet users wouldn't change the calculation more than ad blockers did.

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pixl97 2 hours ago | parent | prev [-]

I mean, back when I was spam filtering setting up a simple Bayesian classifier was easy. Train it on your spam and ham and it worked damned good. "Mission Accomplished".... until it wasn't. Spam rates started climbing and it started getting harder than ever to filter them.

There is always an incentive to get spam to bypass filters, so as your filters increase in accuracy, those attempting to pass said filters adjust their behaviors.

Spammers/cheaters/whateverers will at least just use a second pass filter that uses one of these 'ai scoring' systems to beat said AI scoring systems. So while it's worthwhile to do it at this moment, this window will rapidly close.

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stymaar 2 hours ago | parent [-]

I don't think it's a very good remark, as there's significantly less email spam than 20 years ago.

Another example is ad-blocker-blocker. There was a little bit of an arm race between ad blockers and advertisers in the middle of the 2010s, but it didn't last long. Advertisers mostly just decided not to care about ad-blockers.

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pixl97 an hour ago | parent [-]

>Advertisers mostly just decided not to care about ad-blockers.

Directly not to care because they lost in court.

And yet the biggest advertizer on Earth (Google) decided to change their browser to make adblocking far more difficult. That or they say "just use an app, oh and turn on notifications". I'm not exactly sure who you think won the arms race there, but it seems like we the user did not.

There is significantly more spam than 20 years ago, just less of it reaches your inbox. This is a very important distinction as the cost of spam filtering is just as high as ever. On top of that most people have given up on their own email servers and instead depend on Google/Microsoft to do it for them. This allows these companies to have an overwhelming influence on email on the internet, to the point they can send spam with near impunity, and where if your system does it will be nuked from orbit by their systems.

And much like now Google supplies both the email spam, and the solution to the spam, they'll gladly supply the LLMs spam and the LLM solution while applying their 'flavor' of what's allowed to the entire internet.

	▲	stymaar 43 minutes ago \| parent [-]
		> Directly not to care because they lost in court. I'm pretty sure the illegal sport streaming websites didn't stop doing that just because it became illegal, otherwise they could have stopped their activity altogether while they were at it… > I'm not exactly sure who you think won the arms race there, but it seems like we the user did not. I, at least, won when the webiste showing ads gave up the race (for the past decade at least, only time will tell about the future). > nd yet the biggest advertizer on Earth (Google) decided This is actually an argument in my direction! The owners of websites (which are also the ones posting slop today) didn't care enough and the situation only changed because Google moved. I expect the same thing with slop. Individual websites won't make any effort to make their slop unblockable, and it will only be a problem if OpenAI/Anthropic/Google decide that they care about this market. But unlike Google in the ads market, I don't think the model providers have any reason to care. The web is already dead in their mind anyway. > There is significantly more spam than 20 years ago, just less of it reaches your inbox. This goes against your very argument from earlier! > On top of that most people have given up on their own email servers and instead depend on Google/Microsoft to do it for them. Out of convenience, but you don't need that to be practically free of spam. Whatever version of SpamAssassin is being run on OVH's mail servers has been enough for that purpose for me. > they'll gladly supply the LLMs spam and the LLM solution while applying their 'flavor' of what's allowed to the entire internet. Again, they don't care about the web. They just crawl it for content but they don't want you to read any webpage, they want you to stay in their chatbot. Every other use-case is nonexistent to them (except coding agents, of course, but that's a different product altogether).