Papers by Jeffrey George Wang

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Challenge:	Membership inference attacks are a canonical way to assess a machine learning model’s privacy properties.
Approach:	They propose a framework for principled evaluation of membership inference attacks against large language models by leveraging the insight that training data before and after a fixed point during training are drawn from the same distribution.
Outcome:	The proposed framework can be used to evaluate membership inference attacks against large language models.