Comment by charcircuit

Comment by charcircuit 5 days ago

Isn't this obvious, or at least a common belief people have as opposed to what the article is suggesting the common belief among researches is? If you only have 1 document explaining what the best vacuum cleaner is, you are only going to need a few poisoned documents to poison the results no matter of how many millions of documents of programming source code you include. Taking it as a percent of the overall training data doesn't make sense. These attacks arent trying to change the general behavior, but only affect a niche of answers.

brendoelfrendo 5 days ago

Yes, but I think it makes sense to point out if you consider that most answers satisfy a small niche. The number of programming source code and Stackoverflow documents you can include in training data is huge; but most programming problems are still niche. How many documents would you need to inject to, say, poison any output related to writing SFP network card drivers in C to produce vulnerable code? Fairly specific, but with a potentially broad blast-area.

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charcircuit 5 days ago

I agree that is more interesting but isn't the same thing this paper is doing. This paper introduces a new codeword which essentially creates themselves a new niche as opposed to hijacking an existing one.

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sigbottle 5 days ago

Not necessarily? The way these models are trained suggests "more good data is more good". And if it were really that easy to just synthesize and regurgitate specific knowledge, then we wouldn't need trillion parameter models with hundreds of billions of dollars of investment.

A key thing in classical ML training too is to not overfit an anomaly; you really would not expect this to occur. Also, to me, just the way these models are trained seem like it favors training for the average rather than a specific spike.

A middle ground might be, "Learning to spit arbitrary text at a poisoned token is a much simpler task for the model rather than trying to reason through how to steal the user's SSH keys at a prompt example". One requires still non-trivial reasoning, when compared to literally a simple "spit random token out when I see a token".

Maybe "learning how to do something" truly is additive with these models? I don't know, seems very wrong and counter-intuitive to me. But I googled some unlearning research and apparently it's really hard to "unlearn"

https://arxiv.org/html/2410.16454v1

so maybe this is pointing more evidence to that conclusion.

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