Selective Forgetting: Advancing Machine Unlearning Techniques and
  Evaluation in Language Models
Lingzhi Wang Xingshan Zeng Jinsong Guo Kam-Fai Wong Georg Gottlob
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
Sensitive Information Memory Accuracy (S-MA), designed to gauge the
effectiveness of sensitive information elimination. To reinforce the forgetting
framework, an effective method for annotating sensitive scopes is presented,
involving both online and offline strategies. The online selection mechanism
leverages language probability scores to ensure computational efficiency, while
the offline annotation entails a robust two-stage process based on Large
Language Models (LLMs).