  Demonstration ordering, which is an important strategy for in-context
learning (ICL), can significantly affects the performance of large language
models (LLMs). However, most of the current approaches of ordering require high
computational costs to introduce the priori knowledge. In this paper, inspired
by the human learning process, we propose a simple but effective demonstration
ordering method for ICL, named the few-shot In-Context Curriculum Learning
(ICCL). The ICCL implies gradually increasing the complexity of prompt
demonstrations during the inference process. The difficulty can be assessed by
human experts or LLMs-driven metrics, such as perplexity. Then we design
extensive experiments to discuss the effectiveness of the ICCL at both
corpus-level and instance-level. Moreover, we also investigate the formation
mechanism of LLM's ICCL capability. Experimental results demonstrate that ICCL,
developed during the instruction-tuning stage, is effective for representative
open-source LLMs. To facilitate further research and applications by other
scholars, we make the code publicly available.
