Understanding knowledge mechanisms in Large Language Models (LLMs) is crucial for advancing towards trustworthy AGI. This paper reviews knowledge mechanism analysis from a novel taxonomy including knowledge utilization and evolution. Knowledge utilization delves into the mechanism of memorization, comprehension and application, and creation. Knowledge evolution focuses on the dynamic progression of knowledge within individual and group LLMs. Moreover, we discuss what knowledge LLMs have learned, the reasons for the fragility of parametric knowledge, and the potential dark knowledge (hypothesis) that will be challenging to address. We hope this work can help understand knowledge in LLMs and provide insights for future research.

翻译：理解大型语言模型（LLMs）中的知识机制对于推进可信赖的通用人工智能（AGI）至关重要。本文从知识利用与知识演化这一新颖分类视角，系统综述了知识机制分析的相关研究。知识利用深入探讨了记忆、理解与应用以及创造的内在机制。知识演化则聚焦于个体与群体LLMs中知识的动态演进过程。此外，我们讨论了LLMs已习得的知识类型、参数化知识脆弱性的成因，以及未来可能难以处理的潜在暗知识（假设）。我们希望这项工作有助于深入理解LLMs中的知识本质，并为未来研究提供启示。