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"malicious" fine-tunes are a huge general concern of mine. For instance: - SEO llms - Image/text generation tuned on audience engagement - code exploit gener
by brucethemoose2 4y ago
"malicious" fine-tunes are a huge general concern of mine. For instance:
- SEO llms
- Image/text generation tuned on audience engagement
- code exploit generating llms
- llms trained to avoid spam filters
"countermodels" for a single malicious model are doable, but I think the problem is intractable if training is easy and there are thousands of finetunes floating around.
- barking_biscuit 4y agoTo some extent this is already happening. Or, rather, we've begun doing it to ourselves. At least, in the case of Stable Diffusion, it seems like there is a non-trivial portion of people who are using it to train models for the purpose of generating porn specific to their likes/interests. Which is all fine and dandy, right? Except for the fact that a significant portion of people are actually addicted to it already due to the variety, availability and how it affects the reward system. Couple that with the slot-machine like nature of the variable rewards thrown up by Stable Diffusion, and it's ability to generate higher volumes of stuff that is to your liking, and it's not hard to imagine it will do a real number on some people in the long-run.
- brucethemoose2 4y agoTrue. But I also put that into a different category than models used for malicious intent against other people.
- lmm 4y agoWTF does that have to do with the topic? People are training these models to produce stuff they like, and they're producing stuff they like. That's not a malicious fine-tune, quite the opposite.
- dragonwriter 4y ago> Except for the fact that a significant portion of people are actually addicted to it What’s the basis for this claim?