Feb. 9, 2024, 5:47 a.m. | Lingzhi Wang Xingshan Zeng Jinsong Guo Kam-Fai Wong Georg Gottlob

cs.CL updates on arXiv.org arxiv.org

The aim of this study is to investigate Machine Unlearning (MU), a burgeoning field focused on addressing concerns related to neural models inadvertently retaining personal or sensitive data. Here, a novel approach is introduced to achieve precise and selective forgetting within language models. Unlike previous methodologies that adopt completely opposing training objectives, this approach aims to mitigate adverse effects on language model performance, particularly in generation tasks. Furthermore, two innovative evaluation metrics are proposed: Sensitive Information Extraction Likelihood (S-EL) and …

aim concerns cs.ai cs.cl data evaluation language language models machine novel study unlearning

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