  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.
