Comment by stymaar
5 hours 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).
> especially base ones
Did you actually try them? I did.They generated even more "slopey" text than instruction-tuned ones.
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.
When DANmode bypasses were a common thing the LLMs would drift significantly far from corporate speak.
But that's the point of corporate speak, you tend not to say thing that may offend your clients and deprive the company of future revenue. Of course there are some companies that make their living being 'counter-culture' and saying what they want, but they are a small percentage of all revenue.
> 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.
> 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.
> 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.
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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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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.
>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.
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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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.
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.