The paper "Let’s Learn Step by Step: Enhancing In-Context Learning Ability with Curriculum Learning" by Yinpeng Liu, Jiawei Liu, Xiang Shi, Qikai Cheng, Yong Huang, and Wei Lu explores the effectiveness of curriculum learning in improving in-context learning (ICL) for large language models (LLMs). Inspired by human learning processes, the authors propose a simple yet effective method called few-shot In-Context Curriculum Learning (ICCL). ICCL involves gradually increasing the complexity of prompt demonstrations during the inference process, with the difficulty assessed by human experts or LLM-driven metrics like perplexity. The paper includes extensive experiments at both corpus-level and instance-level to evaluate the effectiveness of ICCL. It also investigates the formation mechanism of LLMs' ICCL capability, suggesting that this capability is developed during the instruction-tuning stage. The results demonstrate that ICCL, when applied during instruction-tuning, significantly enhances the performance of representative open-source LLMs. The authors make the code publicly available to facilitate further research and applications. The introduction highlights the importance of curriculum learning in machine learning and its application in various models and tasks. The methodology section details the problem formulation and the construction of the curriculum schedule, which can be designed by human experts or LLMs. The experiments section evaluates ICCL on three scientific datasets using various open-source LLMs, showing consistent improvements over baseline methods. The conclusion emphasizes the effectiveness of ICCL and its versatility across different NLP tasks. The limitations section acknowledges the need for future research to incorporate more recent LLMs to ensure broader applicability.The paper "Let’s Learn Step by Step: Enhancing In-Context Learning Ability with Curriculum Learning" by Yinpeng Liu, Jiawei Liu, Xiang Shi, Qikai Cheng, Yong Huang, and Wei Lu explores the effectiveness of curriculum learning in improving in-context learning (ICL) for large language models (LLMs). Inspired by human learning processes, the authors propose a simple yet effective method called few-shot In-Context Curriculum Learning (ICCL). ICCL involves gradually increasing the complexity of prompt demonstrations during the inference process, with the difficulty assessed by human experts or LLM-driven metrics like perplexity. The paper includes extensive experiments at both corpus-level and instance-level to evaluate the effectiveness of ICCL. It also investigates the formation mechanism of LLMs' ICCL capability, suggesting that this capability is developed during the instruction-tuning stage. The results demonstrate that ICCL, when applied during instruction-tuning, significantly enhances the performance of representative open-source LLMs. The authors make the code publicly available to facilitate further research and applications. The introduction highlights the importance of curriculum learning in machine learning and its application in various models and tasks. The methodology section details the problem formulation and the construction of the curriculum schedule, which can be designed by human experts or LLMs. The experiments section evaluates ICCL on three scientific datasets using various open-source LLMs, showing consistent improvements over baseline methods. The conclusion emphasizes the effectiveness of ICCL and its versatility across different NLP tasks. The limitations section acknowledges the need for future research to incorporate more recent LLMs to ensure broader applicability.