Extracting Training Data from Large Language Models

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Other Links: arxiv.org

Abstract:

It has become common to publish large (billion parameter) language models that have been trained on private datasets. This paper demonstrates that in such settings, an adversary can perform a training data extraction attack to recover individual training examples by querying the language model. We demonstrate our attack on GPT-2, a la...More

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