In-context learning (ICL) emerges as a promising capability of large language
models (LLMs) by providing them with demonstration examples to perform diverse
tasks. However, the underlying mechanism of how LLMs learn from the provided
context remains under-explored. In this paper, we investigate the working
mechanism of ICL through an information flow lens. Our findings reveal that
label words in the demonstration examples function as anchors: (1) semantic
information aggregates into label word representations during the shallow
computation layers' processing; (2) the consolidated information in label words
serves as a reference for LLMs' final predictions. Based on these insights, we
introduce an anchor re-weighting method to improve ICL performance, a
demonstration compression technique to expedite inference, and an analysis
framework for diagnosing ICL errors in GPT2-XL. The promising applications of
our findings again validate the uncovered ICL working mechanism and pave the
way for future studies.

本文通过信息流视角调查了示例中核心单词在对应学习任务中扮演的角色，并提出了一种锚点加权方法来提高学习效果，一种演示压缩技术来加速推断，并提出了一种分析框架来诊断 ICL 中的错误，并为未来的研究奠定了基础。