Second-Order Information Matters: Revisiting Machine Unlearning for
  Large Language Models
Kang Gu Md Rafi Ur Rashid Najrin Sultana Shagufta Mehnaz
With the rapid development of Large Language Models (LLMs), we have witnessed
intense competition among the major LLM products like ChatGPT, LLaMa, and
Gemini. However, various issues (e.g. privacy leakage and copyright violation)
of the training corpus still remain underexplored. For example, the Times sued
OpenAI and Microsoft for infringing on its copyrights by using millions of its
articles for training. From the perspective of LLM practitioners, handling such
unintended privacy violations can be challenging. Previous work addressed the
``unlearning" problem of LLMs using gradient information, while they mostly
introduced significant overheads like data preprocessing or lacked robustness.
In this paper, contrasting with the methods based on first-order information,
we revisit the unlearning problem via the perspective of second-order
information (Hessian). Our unlearning algorithms, which are inspired by classic
Newton update, are not only data-agnostic/model-agnostic but also proven to be
robust in terms of utility preservation or privacy guarantee. Through a
comprehensive evaluation with four NLP datasets as well as a case study on
real-world datasets, our methods consistently show superiority over the
first-order methods.