  Explainability for Large Language Models (LLMs) is a critical yet challenging
aspect of natural language processing. As LLMs are increasingly integral to
diverse applications, their "black-box" nature sparks significant concerns
regarding transparency and ethical use. This survey underscores the imperative
for increased explainability in LLMs, delving into both the research on
explainability and the various methodologies and tasks that utilize an
understanding of these models. Our focus is primarily on pre-trained
Transformer-based LLMs, such as LLaMA family, which pose distinctive
interpretability challenges due to their scale and complexity. In terms of
existing methods, we classify them into local and global analyses, based on
their explanatory objectives. When considering the utilization of
explainability, we explore several compelling methods that concentrate on model
editing, control generation, and model enhancement. Additionally, we examine
representative evaluation metrics and datasets, elucidating their advantages
and limitations. Our goal is to reconcile theoretical and empirical
understanding with practical implementation, proposing exciting avenues for
explanatory techniques and their applications in the LLMs era.
