  This study investigates the concept of the `right to be forgotten' within the
context of large language models (LLMs). We explore machine unlearning as a
pivotal solution, with a focus on pre-trained models--a notably
under-researched area. Our research delineates a comprehensive framework for
machine unlearning in pre-trained LLMs, encompassing a critical analysis of
seven diverse unlearning methods. Through rigorous evaluation using curated
datasets from arXiv, books, and GitHub, we establish a robust benchmark for
unlearning performance, demonstrating that these methods are over $10^5$ times
more computationally efficient than retraining. Our results show that
integrating gradient ascent with gradient descent on in-distribution data
improves hyperparameter robustness. We also provide detailed guidelines for
efficient hyperparameter tuning in the unlearning process. Our findings advance
the discourse on ethical AI practices, offering substantive insights into the
mechanics of machine unlearning for pre-trained LLMs and underscoring the
potential for responsible AI development.
