Analogical reasoning plays a critical role in human cognition, enabling us to understand new concepts by associating them with familiar ones. Previous research in the AI community has mainly focused on identifying and generating analogies and then examining their quality under human evaluation, which overlooks the practical application of these analogies in real-world settings. Inspired by the human education process, in this paper, we propose to investigate how analogies created by teacher language models (LMs) can assist student LMs in understanding scientific concepts, thereby aligning more closely with practical scenarios. Our results suggest that free-form analogies can indeed aid LMs in understanding concepts. Additionally, analogies generated by student LMs can improve their own performance on scientific question answering, demonstrating their capability to use analogies for self-learning new knowledge. Resources are available at https://github.com/siyuyuan/SCUA.

翻译：类比推理在人类认知中起着关键作用，使我们能够通过将新概念与熟悉概念相关联来理解它们。以往人工智能领域的研究主要集中于识别和生成类比，并在人工评估下检验其质量，这忽视了这些类比在实际场景中的应用。受人类教育过程的启发，本文旨在探究由教师语言模型（LMs）生成的类比如何帮助学生语言模型理解科学概念，从而更贴近实际应用场景。我们的结果表明，自由形式的类比确实能够帮助语言模型理解概念。此外，由学生语言模型生成的类比能够提升其自身在科学问答任务上的表现，证明了它们利用类比进行自我学习新知识的能力。相关资源可在 https://github.com/siyuyuan/SCUA 获取。