This article explores the concept of machine unlearning (MU) in the context of large language models (LLMs), aiming to eliminate undesirable data influence while preserving essential knowledge generation. The study emphasizes the importance of MU in the lifecycle management of LLMs, potentially serving as a foundation for safe, secure, and resource-efficient generative AI. The authors survey existing research, highlight overlooked aspects such as unlearning scope, data-model interaction, and efficacy assessment, and connect MU to related areas like model editing, influence functions, and adversarial training. They propose an effective assessment framework for MU and discuss its applications in copyright and privacy protection, as well as sociotechnical harm reduction. The paper identifies several challenges in applying MU to LLMs, including the difficulty in precisely defining unlearning targets, the scalability of MU techniques, and the need for comprehensive evaluation. It also discusses the importance of unlearning effectiveness, which extends beyond removing specific data points to defining a broader scope for model capability removal. The authors propose mathematical formulations and discuss various unlearning techniques, including model-based and input-based approaches, as well as influence function-based and adversarial training methods. They also explore the relationship between MU and model editing, emphasizing the need for mechanistic approaches to ensure effective and robust unlearning. The study highlights the importance of developing standardized evaluation metrics for MU, noting the need for datasets that can assess the effectiveness of unlearning in various scenarios. It also discusses the challenges of data privacy and the potential for data forgery attacks, emphasizing the need for further research in this area. The paper concludes that MU is a valuable tool for making LLMs more trustworthy, but progress requires updating the unlearning paradigm to address the challenges and opportunities in this field.This article explores the concept of machine unlearning (MU) in the context of large language models (LLMs), aiming to eliminate undesirable data influence while preserving essential knowledge generation. The study emphasizes the importance of MU in the lifecycle management of LLMs, potentially serving as a foundation for safe, secure, and resource-efficient generative AI. The authors survey existing research, highlight overlooked aspects such as unlearning scope, data-model interaction, and efficacy assessment, and connect MU to related areas like model editing, influence functions, and adversarial training. They propose an effective assessment framework for MU and discuss its applications in copyright and privacy protection, as well as sociotechnical harm reduction. The paper identifies several challenges in applying MU to LLMs, including the difficulty in precisely defining unlearning targets, the scalability of MU techniques, and the need for comprehensive evaluation. It also discusses the importance of unlearning effectiveness, which extends beyond removing specific data points to defining a broader scope for model capability removal. The authors propose mathematical formulations and discuss various unlearning techniques, including model-based and input-based approaches, as well as influence function-based and adversarial training methods. They also explore the relationship between MU and model editing, emphasizing the need for mechanistic approaches to ensure effective and robust unlearning. The study highlights the importance of developing standardized evaluation metrics for MU, noting the need for datasets that can assess the effectiveness of unlearning in various scenarios. It also discusses the challenges of data privacy and the potential for data forgery attacks, emphasizing the need for further research in this area. The paper concludes that MU is a valuable tool for making LLMs more trustworthy, but progress requires updating the unlearning paradigm to address the challenges and opportunities in this field.