This paper explores machine unlearning (MU) in the context of large language models (LLMs), aiming to eliminate undesirable data influence while preserving essential knowledge generation. LLM unlearning is envisioned as a critical component in the lifecycle management of LLMs, enabling the development of safe, secure, and resource-efficient generative AI without full retraining. The paper surveys existing research, highlights overlooked aspects such as unlearning scope, data-model interaction, and efficacy assessment, and connects LLM unlearning to related areas like model editing, influence functions, and adversarial training. It proposes an effective assessment framework for LLM unlearning and explores its applications in copyright and privacy protection, as well as sociotechnical harm reduction. The paper identifies key challenges in LLM unlearning, including the difficulty of precisely defining unlearning targets, the scalability of unlearning techniques, and the need for robust and generalizable methods. It also discusses the importance of unlearning effectiveness, which extends beyond removing specific data influences to defining broader scopes for model capability removal. The paper emphasizes the need for more mechanistic methods that ensure effective and robust unlearning while enhancing practicality and feasibility. The paper presents mathematical formulations and discusses design choices for LLM unlearning, including the use of optimization techniques and unlearning responses. It categorizes existing LLM unlearning methods into model-based and input-based approaches, highlighting the strengths and limitations of each. The paper also explores the relationship between LLM unlearning and model editing, emphasizing the importance of localization and the need for more mechanistic approaches. The paper discusses the role of adversarial training in enhancing the robustness of LLM unlearning and the potential of reinforcement learning in developing unlearning paradigms. It also addresses the need for standardized evaluation metrics and datasets to assess the effectiveness of LLM unlearning, including the use of benchmark datasets for harmful content degeneration, personal identification information removal, and copyrighted information prevention. The paper outlines two main application areas of LLM unlearning: copyright and privacy protection, and sociotechnical harm reduction. It discusses the use of LLM unlearning in removing data influence for PII protection and copyrighted information degeneration, as well as in aligning LLMs with human values and preventing harmful outputs. The paper highlights the challenges of unlearning in LLMs, including the difficulty of identifying and removing specific data influences, the need for robust and generalizable methods, and the importance of evaluating unlearning effectiveness across various metrics.This paper explores machine unlearning (MU) in the context of large language models (LLMs), aiming to eliminate undesirable data influence while preserving essential knowledge generation. LLM unlearning is envisioned as a critical component in the lifecycle management of LLMs, enabling the development of safe, secure, and resource-efficient generative AI without full retraining. The paper surveys existing research, highlights overlooked aspects such as unlearning scope, data-model interaction, and efficacy assessment, and connects LLM unlearning to related areas like model editing, influence functions, and adversarial training. It proposes an effective assessment framework for LLM unlearning and explores its applications in copyright and privacy protection, as well as sociotechnical harm reduction. The paper identifies key challenges in LLM unlearning, including the difficulty of precisely defining unlearning targets, the scalability of unlearning techniques, and the need for robust and generalizable methods. It also discusses the importance of unlearning effectiveness, which extends beyond removing specific data influences to defining broader scopes for model capability removal. The paper emphasizes the need for more mechanistic methods that ensure effective and robust unlearning while enhancing practicality and feasibility. The paper presents mathematical formulations and discusses design choices for LLM unlearning, including the use of optimization techniques and unlearning responses. It categorizes existing LLM unlearning methods into model-based and input-based approaches, highlighting the strengths and limitations of each. The paper also explores the relationship between LLM unlearning and model editing, emphasizing the importance of localization and the need for more mechanistic approaches. The paper discusses the role of adversarial training in enhancing the robustness of LLM unlearning and the potential of reinforcement learning in developing unlearning paradigms. It also addresses the need for standardized evaluation metrics and datasets to assess the effectiveness of LLM unlearning, including the use of benchmark datasets for harmful content degeneration, personal identification information removal, and copyrighted information prevention. The paper outlines two main application areas of LLM unlearning: copyright and privacy protection, and sociotechnical harm reduction. It discusses the use of LLM unlearning in removing data influence for PII protection and copyrighted information degeneration, as well as in aligning LLMs with human values and preventing harmful outputs. The paper highlights the challenges of unlearning in LLMs, including the difficulty of identifying and removing specific data influences, the need for robust and generalizable methods, and the importance of evaluating unlearning effectiveness across various metrics.