To Forget or Not? Towards Practical Knowledge Unlearning for Large
  Language Models
Authors: name: Bozhong Tian, name: Xiaozhuan Liang, name: Siyuan Cheng, name: Qingbin Liu, name: Mengru Wang, name: Dianbo Sui, name: Xi Chen, name: Huajun Chen, name: Ningyu Zhang

Large Language Models (LLMs) trained on extensive corpora inevitably retain
sensitive data, such as personal privacy information and copyrighted material.
Recent advancements in knowledge unlearning involve updating LLM parameters to
erase specific knowledge. However, current unlearning paradigms are mired in
vague forgetting boundaries, often erasing knowledge indiscriminately. In this
work, we introduce KnowUnDo, a benchmark containing copyrighted content and
user privacy domains to evaluate if the unlearning process inadvertently erases
essential knowledge. Our findings indicate that existing unlearning methods
often suffer from excessive unlearning. To address this, we propose a simple
yet effective method, MemFlex, which utilizes gradient information to precisely
target and unlearn sensitive parameters. Experimental results show that MemFlex
is superior to existing methods in both precise knowledge unlearning and
general knowledge retaining of LLMs. Code and dataset will be released at
https://github.com/zjunlp/KnowUnDo.