  Understanding in-context learning (ICL) capability that enables large
language models (LLMs) to excel in proficiency through demonstration examples
is of utmost importance. This importance stems not only from the better
utilization of this capability across various tasks, but also from the
proactive identification and mitigation of potential risks, including concerns
regarding truthfulness, bias, and toxicity, that may arise alongside the
capability. In this paper, we present a thorough survey on the interpretation
and analysis of in-context learning. First, we provide a concise introduction
to the background and definition of in-context learning. Then, we give an
overview of advancements from two perspectives: 1) a theoretical perspective,
emphasizing studies on mechanistic interpretability and delving into the
mathematical foundations behind ICL; and 2) an empirical perspective,
concerning studies that empirically analyze factors associated with ICL. We
conclude by highlighting the challenges encountered and suggesting potential
avenues for future research. We believe that our work establishes the basis for
further exploration into the interpretation of in-context learning.
Additionally, we have created a repository containing the resources referenced
in our survey.
