I’m not an expert, but I would say that it is going to be less likely for a diffusion model to spit out training data in a completely intact way. The way that LLMs versus diffusion models work are very different.
LLMs work by predicting the next statistically likely token, they take all of the previous text, then predict what the next token will be based on that. So, if you can trick it into a state where the next subsequent tokens are something verbatim from training data, then that’s what you get.
Diffusion models work by taking a randomly generated latent, combining it with the CLIP interpretation of the user’s prompt, then trying to turn the randomly generated information into a new latent which the VAE will then decode into something a human can see, because the latents the model is dealing with are meaningless numbers to humans.
In other words, there’s a lot more randomness to deal with in a diffusion model. You could probably get a specific source image back if you specially crafted a latent and a prompt, which one guy did do by basically running img2img on a specific image that was in the training set and giving it a prompt to spit the same image out again. But that required having the original image in the first place, so it’s not really a weakness in the same way this was for GPT.
But the fact is the LLM was able to spit out the training data. This means that anything in the training data isn’t just copied into the training dataset, allegedly under fair use as research, but also copied into the LLM as part of an active commercial product. Sure, the LLM might break it down and store the components separately, but if an LLM can reassemble it and spit out the original copyrighted work then how is that different from how a photocopier breaks down the image scanned from a piece of paper then reassembles it into instructions for its printer?
But the thing is the law has already established this with people and their memories. You might genuinely not realise you’re plagiarising, but what matters is the similarity of the work produced.
ChatGPT has copied the data into its training database, then trained off that database, then it runs “independently” of that database - which is how they vaguely argue fair use under the research exemption.
However if ChatGPT can “remember” its training data and recompile significant portions of it in certain circumstances, then it must be guilty of plagiarism and copyright infringement.
Speaking for LLMs, given that they operate on a next-token basis, there will be some statistical likelihood of spitting out original training data that can’t be avoided. The normal counter-argument being that in theory, the odds of a particular piece of training data coming back out intact for more than a handful of words should be extremely low.
Of course, in this case, Google’s researchers took advantage of the repeat discouragement mechanism to make that unlikelihood occur reliably, showing that there are indeed flaws to make it happen.
If a person studies a text then writes an article about the same subject as that text while using the same wording and discussing the same points, then it’s plagiarism whether or not they made an exact copy. Surely it should also be the case with LLM’s, which train on the data then inadvertently replicate the data again? The law has already established that it doesn’t matter what the process is for making the new work, what matters is how close it is to the original work.
I’m not an expert, but I would say that it is going to be less likely for a diffusion model to spit out training data in a completely intact way. The way that LLMs versus diffusion models work are very different.
LLMs work by predicting the next statistically likely token, they take all of the previous text, then predict what the next token will be based on that. So, if you can trick it into a state where the next subsequent tokens are something verbatim from training data, then that’s what you get.
Diffusion models work by taking a randomly generated latent, combining it with the CLIP interpretation of the user’s prompt, then trying to turn the randomly generated information into a new latent which the VAE will then decode into something a human can see, because the latents the model is dealing with are meaningless numbers to humans.
In other words, there’s a lot more randomness to deal with in a diffusion model. You could probably get a specific source image back if you specially crafted a latent and a prompt, which one guy did do by basically running img2img on a specific image that was in the training set and giving it a prompt to spit the same image out again. But that required having the original image in the first place, so it’s not really a weakness in the same way this was for GPT.
But the fact is the LLM was able to spit out the training data. This means that anything in the training data isn’t just copied into the training dataset, allegedly under fair use as research, but also copied into the LLM as part of an active commercial product. Sure, the LLM might break it down and store the components separately, but if an LLM can reassemble it and spit out the original copyrighted work then how is that different from how a photocopier breaks down the image scanned from a piece of paper then reassembles it into instructions for its printer?
It’s not copied as is, thing is a bit more complicated as was already pointed out
But the thing is the law has already established this with people and their memories. You might genuinely not realise you’re plagiarising, but what matters is the similarity of the work produced.
ChatGPT has copied the data into its training database, then trained off that database, then it runs “independently” of that database - which is how they vaguely argue fair use under the research exemption.
However if ChatGPT can “remember” its training data and recompile significant portions of it in certain circumstances, then it must be guilty of plagiarism and copyright infringement.
Speaking for LLMs, given that they operate on a next-token basis, there will be some statistical likelihood of spitting out original training data that can’t be avoided. The normal counter-argument being that in theory, the odds of a particular piece of training data coming back out intact for more than a handful of words should be extremely low.
Of course, in this case, Google’s researchers took advantage of the repeat discouragement mechanism to make that unlikelihood occur reliably, showing that there are indeed flaws to make it happen.
If a person studies a text then writes an article about the same subject as that text while using the same wording and discussing the same points, then it’s plagiarism whether or not they made an exact copy. Surely it should also be the case with LLM’s, which train on the data then inadvertently replicate the data again? The law has already established that it doesn’t matter what the process is for making the new work, what matters is how close it is to the original work.