This paper provides a comprehensive survey of recent advancements in continual learning for large language models (LLMs). Due to the high training costs associated with LLMs, frequent retraining is impractical. However, updates are necessary to keep LLMs up-to-date with evolving human knowledge and values. The authors categorize continual learning techniques into three stages: continual pretraining, instruction tuning, and alignment. They contrast these techniques with simpler adaptation methods used in smaller models and other enhancement strategies like retrieval-augmented generation and model editing. The paper also discusses benchmarks and evaluation methods, highlighting challenges and future directions in this field. Key challenges include computational efficiency, social responsibility, automatic learning, controllable forgetting, history tracking, and theoretical insights. The authors aim to provide a detailed understanding of how to effectively implement continual learning in LLMs, contributing to the development of more advanced and adaptable language models.This paper provides a comprehensive survey of recent advancements in continual learning for large language models (LLMs). Due to the high training costs associated with LLMs, frequent retraining is impractical. However, updates are necessary to keep LLMs up-to-date with evolving human knowledge and values. The authors categorize continual learning techniques into three stages: continual pretraining, instruction tuning, and alignment. They contrast these techniques with simpler adaptation methods used in smaller models and other enhancement strategies like retrieval-augmented generation and model editing. The paper also discusses benchmarks and evaluation methods, highlighting challenges and future directions in this field. Key challenges include computational efficiency, social responsibility, automatic learning, controllable forgetting, history tracking, and theoretical insights. The authors aim to provide a detailed understanding of how to effectively implement continual learning in LLMs, contributing to the development of more advanced and adaptable language models.