Protecting Privacy Through Approximating Optimal Parameters for Sequence Unlearning in Language Models

Although language models (LMs) demonstrate exceptional capabilities on various tasks, they are potentially vulnerable to extraction attacks, which represent a significant privacy risk. To mitigate the privacy concerns of LMs, machine unlearning has emerged as an important research area, which is uti...

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Hauptverfasser:	Lee, Dohyun, Rim, Daniel, Choi, Minseok, Choo, Jaegul
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Computation and Language
Online-Zugang:	Volltext bestellen
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