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Understanding emergent abilities, such as in-context learning (ICL) andchain-of-thought (CoT) prompting in large language models (LLMs), is of utmostimportance. This importance stems not only from the better utilization of thesecapabilities across various tasks, but also from the proactive identificationand mitigation of potential risks, including concerns of truthfulness, bias,and toxicity, that may arise alongside these capabilities. In this paper, wepresent a thorough survey on the interpretation and analysis of emergentabilities of LLMs. First, we provide a concise introduction to the backgroundand definition of emergent abilities. Then, we give an overview of advancementsfrom two perspectives: 1) a macro perspective, emphasizing studies on themechanistic interpretability and delving into the mathematical foundationsbehind emergent abilities; and 2) a micro-perspective, concerning studies thatfocus on empirical interpretability by examining factors associated with theseabilities. We conclude by highlighting the challenges encountered andsuggesting potential avenues for future research. We believe that our workestablishes the basis for further exploration into the interpretation ofemergent abilities.

The Mystery and Fascination of LLMs: A Comprehensive Survey on the Interpretation and Analysis of Emergent Abilities