Large Language Models (LLMs) have shown strong in-context learning (ICL) abilities with a few demonstrations. However, one critical challenge is how to select demonstrations to elicit the full potential of LLMs. In this paper, we propose Curriculum Demonstration Selection (CDS), a novel demonstration selection method for ICL. Instead of merely using similarity, CDS additionally partitions samples by their complexity measurements. Following curriculum learning, CDS then selects demonstrations from easy to difficult. Thus the selected demonstrations cover a wide range of difficulty levels, enabling LLMs to learn from varied complexities within the training set. Experiments demonstrate that our CDS consistently outperforms baseline methods, achieving notable improvements across nine LLMs on three benchmarks. Moreover, CDS proves especially effective in enhancing LLM performance in solving challenging problems.

该研究解决了在大语言模型的上下文学习中，如何选择示范以充分发挥其潜力的问题。提出的课程示范选择（CDS）方法，通过对样本复杂度进行划分，从易到难地选择示范，从而涵盖多种难度水平，显著提高了大语言模型在解决复杂问题时的性能。

上下文学习的课程示范选择