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WD-42 9 hours ago

https://news.ycombinator.com/item?id=49249269

I put the "humanized" output through Pangram and it still comes out as 100% AI generated.

jchw 8 hours ago | parent | next [-]

To be honest with you, I don't think I would be able to identify with high certainty that the bottom text is AI generated, so it definitely goes a long way to obscure the AI-generated nature of it, but I also think it still feels unnatural somehow. I realize my framing naturally calls into question whether I'm being honest, but I am being honest. Given my experience with similar "skills" (it's just chunks of prompt, nothing magical after all) I expected even less.

But still, this is all very strange because it wasn't that many generations of AI models ago that AI writing was a lot better - I'm talking GPT 4.1, Claude 4.5, that sort of era.

Anthropic newsroom posts on the other hand are carefully constructed and well-written in a way that I have not seen demonstrated by LLMs yet, past or present. I expect that they have well-paid staff who are careful with every detail of their public communications. When you put it that way, it almost feels unfathomable that they wouldn't, doesn't it?

sebmellen an hour ago | parent [-]

GPT 4.5 was really good.

breezybottom 8 hours ago | parent | prev [-]

That's about as useful as saying you asked the magical sky fairy.

meowface 7 hours ago | parent | next [-]

Pangram has an extremely low false positive rate. Even on adversarial examples.

One trade-off is even some obviously LLM text won't get detected by them, but they work really hard to ensure false positives are rare since a false accusation is much worse for society than someone getting away with LLM meatpuppetry.

jchw 8 hours ago | parent | prev [-]

I think you can't trust Pangram in a high stakes situation, but it is absolutely better than random noise at detecting AI-generated text. Which isn't surprising. If the distribution of probabilities can yield blatant Claudisms, it's not surprising it would also have more subtle deviations.

(Addendum: As I recall, LLM-generated outputs roughly follow Zipf's law, but the distribution still tends to have some subtle distinctions vs human text; pretty interesting, but I don't know where I heard this, so nothing to cite. Sorry.)