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Planting and Mitigating Memorized Content in Predictive-Text Language Models. (arXiv:2212.08619v1 [cs.CL])
Dec. 19, 2022, 2:10 a.m. | C.M. Downey, Wei Dai, Huseyin A. Inan, Kim Laine, Saurabh Naik, Tomasz Religa
cs.CR updates on arXiv.org arxiv.org
Language models are widely deployed to provide automatic text completion
services in user products. However, recent research has revealed that language
models (especially large ones) bear considerable risk of memorizing private
training data, which is then vulnerable to leakage and extraction by
adversaries. In this study, we test the efficacy of a range of
privacy-preserving techniques to mitigate unintended memorization of sensitive
user text, while varying other factors such as model size and adversarial
conditions. We test both "heuristic" mitigations …
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