We take a number of steps to reduce the risk that our models are used in a way that could
violate a person’s privacy rights. These include ﬁne-tuning models to reject these types of requests,
removing personal information from the training dataset where feasible, creating automated model
evaluations, monitoring and responding to user attempts to generate this type of information, and
restricting this type of use in our terms and policies. Our eﬀorts to expand context length and
improve embedding models for retrieval may help further limit privacy risks moving forward by
tying task performance more to the information a user brings to the model. We continue to research,
develop, and enhance technical and process mitigations in this area.
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