Knowledge plays a critical role in artificial intelligence. Recently, the extensive success of pre-trained language models (PLMs) has raised significant attention about how knowledge can be acquired, maintained, updated and used by language models. Despite the enormous amount of related studies, there still lacks a unified view of how knowledge circulates within language models throughout the learning, tuning, and application processes, which may prevent us from further understanding the connections between current progress or realizing existing limitations. In this survey, we revisit PLMs as knowledge-based systems by dividing the life circle of knowledge in PLMs into five critical periods, and investigating how knowledge circulates when it is built, maintained and used. To this end, we systematically review existing studies of each period of the knowledge life cycle, summarize the main challenges and current limitations, and discuss future directions.

翻译：知识在人工智能中扮演着关键角色。近年来，预训练语言模型（PLMs）的巨大成功引发了广泛关注，探讨语言模型如何获取、维护、更新和使用知识。尽管已有大量相关研究，但关于知识在学习、微调和应用过程中如何在语言模型内部循环，仍缺乏统一的视角，这可能会阻碍我们进一步理解当前进展之间的联系，或认识到现有局限性。在本综述中，我们将PLMs重新视为基于知识的系统，将PLMs中知识的生命周期划分为五个关键阶段，并探究知识在构建、维护和使用过程中的循环方式。为此，我们系统回顾了知识生命周期每个阶段的现有研究，总结了主要挑战和当前局限，并探讨了未来方向。