Large language models (LLMs) have shown an impressive ability to perform a
wide range of tasks using in-context learning (ICL), where a few examples are
used to describe a task to the model. However, the performance of ICL varies
significantly with the choice of demonstrations, and it is still unclear why
this happens or what factors will influence its choice. In this work, we first
revisit the factors contributing to this variance from both data and model
aspects, and find that the choice of demonstration is both data- and
model-dependent. We further proposed a data- and model-dependent demonstration
selection method, \textbf{TopK + ConE}, based on the assumption that
\textit{the performance of a demonstration positively correlates with its
contribution to the model's understanding of the test samples}, resulting in a
simple and effective recipe for ICL. Empirically, our method yields consistent
improvements in both language understanding and generation tasks with different
model scales. Further analyses confirm that, besides the generality and
stability under different circumstances, our method provides a unified
explanation for the effectiveness of previous methods. Code will be released.

大型语言模型通过上下文学习（ICL）展示出了在各种任务中都具备惊人的能力，本研究探讨了影响 ICL 性能的因素，并提出了一种基于数据和模型的演示选择方法，该方法通过性能与模型对测试样本的理解相关联来进行演示选择，从而在语言理解和生成任务中实现了一致的改进。