It’s long been proven that LLMs can produce exact copies of their training data. Not every piece of training data, of course, but enough where spitting out exact replicas of proprietary code is a legitimate concern. Not to mention that copies don’t need to be exact in order to be a problem.
The models are essentially a giant lossy compression applied to the training data.
They don’t store the loseless version of the training data sure, but that doesn’t mean it can’t reproduce something close enough to it, it’s literally how the whole thing works.
My understanding of it is that it’s far more complex than “lossy compression”. Of course it can generate text that is close to something in its training data, but I don’t think people have the right mental model if they think the model itself somehow has whole chunks of code in it. It does not.
My understanding of it is that it’s far more complex than “lossy compression”. Of course it can generate text that is close to something in its training data, but I don’t think people have the right mental model if they think the model itself somehow has whole chunks of code in it. It does not.
I’ve never seen any proof of this? They can fetch data and present it as their own maybe? But models don’t store training data verbatim. It’s like 6+6 and 4+8 both = 12, but they just store 12 and how it got there is unknown.
I’ve never seen any proof of this? They can fetch data and present it as their own maybe? But models don’t store training data verbatim. It’s like 6+6 and 4+8 both = 12, but they just store 12 and how it got there is unknown.
The models don’t store access to their training data. So no, there’s no source code in the model to retrieve.
It’s long been proven that LLMs can produce exact copies of their training data. Not every piece of training data, of course, but enough where spitting out exact replicas of proprietary code is a legitimate concern. Not to mention that copies don’t need to be exact in order to be a problem.
The models are essentially a giant lossy compression applied to the training data.
They don’t store the loseless version of the training data sure, but that doesn’t mean it can’t reproduce something close enough to it, it’s literally how the whole thing works.
My understanding of it is that it’s far more complex than “lossy compression”. Of course it can generate text that is close to something in its training data, but I don’t think people have the right mental model if they think the model itself somehow has whole chunks of code in it. It does not.
My understanding of it is that it’s far more complex than “lossy compression”. Of course it can generate text that is close to something in its training data, but I don’t think people have the right mental model if they think the model itself somehow has whole chunks of code in it. It does not.
So why can they recreate entire articles from the New Yorker and complete functions from the training set? Don’t fall for the slop laundering.
I’ve never seen any proof of this? They can fetch data and present it as their own maybe? But models don’t store training data verbatim. It’s like 6+6 and 4+8 both = 12, but they just store 12 and how it got there is unknown.
I’ve never seen any proof of this? They can fetch data and present it as their own maybe? But models don’t store training data verbatim. It’s like 6+6 and 4+8 both = 12, but they just store 12 and how it got there is unknown.