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.
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.