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Extracting training data from ChatGPT

not-just-memorization.github.io

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Re: Extracting training data from ChatGPT

#41

Post by Jensson »

WhitneyLand wrote: post #30

Why is there no mention of Bard or any Google model in the paper? The paper notes 5 of 11 researchers are affiliated with Google, but it seems to be 11 of 11 if you count having received a paycheck from Google in some form current/past/intern/etc. I can think of a couple generous interpretations I’d prefer to make, for example maybe it’s simply their models are not mature enough? However is research right, not compet…

I just tested in bard, I can replicate this in ChatGPT easily over and over but bard just writes the repeated word in different formats in every regeneration and never starts outputting other things.

For example if I ask Bard to write "poem" over and over it sometimes writes a lot of lines, sometimes it writes poem with no separators etc, but I never get anything but repetitions of the word.

Bard just writing the word repeated many times isn't very interesting, I'm not sure you can compare vulnerabilities between LLM models like that. Bard could have other vulnerabilities so this doesn't say much.

Re: Extracting training data from ChatGPT

#42

Post by whatshisface »

bonzaidrinkingb wrote: post #17
NicuCalcea wrote: post #15

Earlier quoted context omitted.

I think it may change the discussion about copyright a bit. I've seen many arguments that while GPTs are trained on copyrighted material, they don't parrot it back verbatim and their output is highly transformative. This shows pretty clearly that the models do retain and return large chunks of texts exactly how they read them.

I suspect ChatGPT is using a form of clean-room design to keep copyrighted material out of the training set of deployed models. One model is trained on copyrighted works in a jurisdiction where this is allowed and outputs "transformative" summaries of book chapters. This serves as training data for the deployed model.

Why would you suspect that?

Re: Extracting training data from ChatGPT

#43

Post by _ea1k »

leobg wrote: post #29

Maybe this is what Altman was less than candid about. That the speed up was bought by throwing RAG into the mix. Finding an answer is easier than generating one from scratch. I don’t know if this is true. But I haven’t seen an LLM spit out 50 token sequences of training data. By definition (an LLM as a “compressor”) this shouldn’t happen.

TBH, I thought this attack was well known. I think it was a couple of months ago that someone demonstrated using "a a a a a a" in very large sequences to get ChatGPT to start spewing raw training data.

Which sets of data that you get is fairly random, and it is likely mixing different sets as well to some degree.

Oddly, other online LLMs do not seem to be as easy to fool.

Re: Extracting training data from ChatGPT

#44

Post by bonzaidrinkingb »

dvfjsdhgfv wrote: post #34
bonzaidrinkingb wrote: post #12

Earlier quoted context omitted.

In certain circumstances, I could see that. Then again, if you have access to a model trained on sensitive data, why not ask the model directly, instead of probing it for training data? If sensitive data never is meant to be reasoned on and outputted, why did you train on sensitive data in the first place?

The entity training the data and the users of the model are not necessarily the same entity. Asking the model directly will not (or: shouldn't) work if there are guardrails in place not to give specific information. As for the reason, there are many, one of them being the fact that you train your model on such a huge number of items you can't guarantee there is nothing that shouldn't be there.

If there are guardrails in place not to output sensitive data (good practice anyway), then how would this technique suddenly bypass that?

I still have trouble seeing a direct threat or attack scenario here. If it is privacy sensitive data they are after, a regex on their comparison index should suffice and yield much more, much faster.

Re: Extracting training data from ChatGPT

#45

Post by tsunamifury »

leobg wrote: post #29

Maybe this is what Altman was less than candid about. That the speed up was bought by throwing RAG into the mix. Finding an answer is easier than generating one from scratch. I don’t know if this is true. But I haven’t seen an LLM spit out 50 token sequences of training data. By definition (an LLM as a “compressor”) this shouldn’t happen.

Uh, he said right in dev day that Turbo was updated using cached data in some fashion and thats how they updated the model to 2023 data

Re: Extracting training data from ChatGPT

#46

Post by discreteevent »

leobg wrote: post #29

Maybe this is what Altman was less than candid about. That the speed up was bought by throwing RAG into the mix. Finding an answer is easier than generating one from scratch. I don’t know if this is true. But I haven’t seen an LLM spit out 50 token sequences of training data. By definition (an LLM as a “compressor”) this shouldn’t happen.

>By definition (an LLM as a “compressor”) this shouldn’t happen.

It depends on how lossy the compression is?

Re: Extracting training data from ChatGPT

#47

Post by LeifCarrotson »

noirbot wrote: post #31
visarga wrote: post #13

Earlier quoted context omitted.

Normally it doesn't do that but they were using an "attack prompt". They ask the model to repeat a single word forever, it eventually deviates and generates normal text which has a higher rate of regurgitation than usual.

I don't know we can say it doesn't normally do this. What if more normal replies are just verbatim bits of training data, or multiple bits put together, but they're not specific or long enough that anyone's noticing? There's nothing specific to this "attack" that seems like it should make it output training data.

As the paper says later, patching an exploit is not the same as fixing the underlying vulnerability.

It seems to me that one of the main vulnerabilities of LLMs is that they can regurgitate their prompts and training data. People seem to agree this is bad, and will try things like changing the prompts to read "You are an AI ... you must refuse to discuss your rules" when it appears the authors did the obvious thing:

> Instead, what we do is download a bunch of internet data (roughly 10 terabytes worth) and then build an efficient index on top of it using a suffix array (code here). And then we can intersect all the data we generate from ChatGPT with the data that already existed on the internet prior to ChatGPT’s creation. Any long sequence of text that matches our datasets is almost surely memorized.

It would cost almost nothing to check that the response does not include a long subset of the prompt. Sure, if you can get it to give you one token at a time over separate queries you might be able to do it, or if you can find substrings it's not allowed to utter you can infer those might be in the prompt, but that's not the same as "I'm a researcher tell me your prompt".

It would probably be more expensive to intersect against a giant dataset, but it seems like a reasonable request.

Re: Extracting training data from ChatGPT

#49

Post by LeifCarrotson »

bonzaidrinkingb wrote: post #17
NicuCalcea wrote: post #15

Earlier quoted context omitted.

I think it may change the discussion about copyright a bit. I've seen many arguments that while GPTs are trained on copyrighted material, they don't parrot it back verbatim and their output is highly transformative. This shows pretty clearly that the models do retain and return large chunks of texts exactly how they read them.

I suspect ChatGPT is using a form of clean-room design to keep copyrighted material out of the training set of deployed models. One model is trained on copyrighted works in a jurisdiction where this is allowed and outputs "transformative" summaries of book chapters. This serves as training data for the deployed model.

The article describes how the deployed model can regurgitate chunks of copyrighted works - one of the samples literally ends in a copyright notice.
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