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Comment by Uehreka | Hacker News Reader

What makes you think that LLMs will be better at combating spam than they are at creating it? There’s no universal rule that innovations in AI will go hand in hand with innovations in detecting AI, yet I feel like I see people talking all the time like that’s the case.

As of right now, LLMs are prolific but unreliable, which makes them extremely well suited for generating spam, but unsuited to detecting it without a large number of false positives and negatives.

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I honestly don’t mind so much if it detects “ai-generated” vs. “human-generated”—the key thing to detect is whether the page is full of irrelevant SEO junk. GP suggested several attributes that ought to be detectable. Even if we don't eliminate the AI content, but succeed in promoting "better" content, maybe it's an improvement?

Classification is easier than generation

Yeah no I’m gonna need more than that chief. Everything I know about LLMs says the opposite.

Look up Generative Adversarial Networks, that's their basic principle.

You're not classifying just text, you're classifying entire web pages.

I'd say the whole point of GAN is that generation is cheaper than classification, therefore an effective brute-force way of making a good classifier is to generate an infinite supply of examples with a-priori known classification, and pit it against a classifier.

Yeah, but the theoretical endpoint of training a GAN is that the generator gets so good that the discriminator has to resort to guesses and become unable to tell with any sort of accuracy as to whether the example it is shown is real or generated.

I don't think that ever happens in training, at least in the image domain. The classifier can always can find some subtle clue.

Sadly, this didn't seem to track for LLMs. Even OpenAI gave up on trying to detect its own outputs.

> As of July 20, 2023, the AI classifier is no longer available due to its low rate of accuracy. We are working to incorporate feedback and are currently researching more effective provenance techniques for text, and have made a commitment to develop and deploy mechanisms that enable users to understand if audio or visual content is AI-generated.

You're not classifying just text, you're classifying entire web pages. If it's easy to tell for a human that it's SEO spam, it's easy for a classifier.

Not with standard LLM models. Generating garbage is easier; deciding that it’s garbage much more difficult. This is an inherent problem with “prompt optimizers” like DSPy.

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