An attack that recovers training data from GPT-2.http://feedproxy.google.com/~r/blogspot/gJZg/~3/Qm9ni79lltY/privacy-considerations-in-large.html …
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That makes sense. I’m curious if you can measure this? Like, if you generate billions of examples, do you get less diversity than you would expect from a model with “true” ability to generate novel text? I recall seeing some research along those lines
Measuring generalization is hard, and gets harder the more data the model is trained on. We don't have a good way to do it right now, nor is there much research interest in the topic (but there should be)
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Btw I think this was the research I was thinking about:http://www.offconvex.org/2017/07/06/GANs3/ …
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