This study investigates the concept of the 'right to be forgotten' in the context of large language models (LLMs), focusing on machine unlearning for pre-trained models. The research proposes a comprehensive framework for machine unlearning in pre-trained LLMs, analyzing seven diverse unlearning methods. Through evaluation on curated datasets from arXiv, books, and GitHub, the study demonstrates that these methods are over 10^5 times more computationally efficient than retraining. The results show that integrating gradient ascent with gradient descent on in-distribution data improves hyperparameter robustness. The study also provides guidelines for efficient hyperparameter tuning in the unlearning process. The findings contribute to ethical AI practices, offering insights into the mechanics of machine unlearning for pre-trained LLMs and highlighting the potential for responsible AI development. The paper addresses the challenge of unlearning pre-trained LLMs, which is more complex than unlearning fine-tuned models due to the large scale of pre-training data. The study proposes an approximate retraining method using an in-distribution, unseen dataset to simulate the performance of a retraining baseline. The research also evaluates unlearning methods on three domains: arXiv papers, GitHub code repositories, and books. The results show that unlearning methods can effectively reduce the model's ability to predict on the forget set while maintaining performance on the retain set and downstream tasks. The study finds that combining gradient ascent with gradient descent on in-distribution data enhances hyperparameter robustness. The computational efficiency of unlearning is also analyzed, showing that it is approximately 10^5 times more efficient than retraining in terms of computational resources. The study also explores the effectiveness of unlearning methods in reducing privacy leakage related to membership inference. The results show that unlearning methods can make it more challenging to differentiate between the forget set and unseen data, indicating their effectiveness in reducing privacy leakage. The study also discusses the limitations of the work, including the focus on the Yi-6B model and the challenges of collecting forget sets due to the lack of open-source pre-training data. The study encourages future research to investigate the applicability of unlearning processes to other models, including larger models and more complex architectures. The study also highlights the need for more principled theoretical investigations of unlearning in LLMs and the development of more powerful membership inference attack methods. The study emphasizes the practical aspects of approximate unlearning in LLMs and the challenges of distinguishing between member and non-member data in LLMs. The study also encourages researchers and developers to use the methods responsibly and ethically.This study investigates the concept of the 'right to be forgotten' in the context of large language models (LLMs), focusing on machine unlearning for pre-trained models. The research proposes a comprehensive framework for machine unlearning in pre-trained LLMs, analyzing seven diverse unlearning methods. Through evaluation on curated datasets from arXiv, books, and GitHub, the study demonstrates that these methods are over 10^5 times more computationally efficient than retraining. The results show that integrating gradient ascent with gradient descent on in-distribution data improves hyperparameter robustness. The study also provides guidelines for efficient hyperparameter tuning in the unlearning process. The findings contribute to ethical AI practices, offering insights into the mechanics of machine unlearning for pre-trained LLMs and highlighting the potential for responsible AI development. The paper addresses the challenge of unlearning pre-trained LLMs, which is more complex than unlearning fine-tuned models due to the large scale of pre-training data. The study proposes an approximate retraining method using an in-distribution, unseen dataset to simulate the performance of a retraining baseline. The research also evaluates unlearning methods on three domains: arXiv papers, GitHub code repositories, and books. The results show that unlearning methods can effectively reduce the model's ability to predict on the forget set while maintaining performance on the retain set and downstream tasks. The study finds that combining gradient ascent with gradient descent on in-distribution data enhances hyperparameter robustness. The computational efficiency of unlearning is also analyzed, showing that it is approximately 10^5 times more efficient than retraining in terms of computational resources. The study also explores the effectiveness of unlearning methods in reducing privacy leakage related to membership inference. The results show that unlearning methods can make it more challenging to differentiate between the forget set and unseen data, indicating their effectiveness in reducing privacy leakage. The study also discusses the limitations of the work, including the focus on the Yi-6B model and the challenges of collecting forget sets due to the lack of open-source pre-training data. The study encourages future research to investigate the applicability of unlearning processes to other models, including larger models and more complex architectures. The study also highlights the need for more principled theoretical investigations of unlearning in LLMs and the development of more powerful membership inference attack methods. The study emphasizes the practical aspects of approximate unlearning in LLMs and the challenges of distinguishing between member and non-member data in LLMs. The study also encourages researchers and developers to use the methods responsibly and ethically.