Grasping complex computing concepts often poses a challenge for students who
struggle to anchor these new ideas to familiar experiences and understandings.
To help with this, a good analogy can bridge the gap between unfamiliar
concepts and familiar ones, providing an engaging way to aid understanding.
However, creating effective educational analogies is difficult even for
experienced instructors. We investigate to what extent large language models
(LLMs), specifically ChatGPT, can provide access to personally relevant
analogies on demand. Focusing on recursion, a challenging threshold concept, we
conducted an investigation analyzing the analogies generated by more than 350
first-year computing students. They were provided with a code snippet and
tasked to generate their own recursion-based analogies using ChatGPT,
optionally including personally relevant topics in their prompts. We observed a
great deal of diversity in the analogies produced with student-prescribed
topics, in contrast to the otherwise generic analogies, highlighting the value
of student creativity when working with LLMs. Not only did students enjoy the
activity and report an improved understanding of recursion, but they described
more easily remembering analogies that were personally and culturally relevant.

将复杂的计算概念与熟悉的经验和理解相联系通常是学生面临的挑战之一。为了帮助学生更好地理解，一个好的类比可以弥补陌生概念与熟悉概念之间的差距，提供一种有趣的学习方式。本研究调查了大型语言模型（特别是 ChatGPT）是否能够按需提供与个人相关的类比。通过对超过 350 名首年计算机学生生成的类比进行分析，研究集中于递归这一具有挑战性的门槛概念。结果表明，在学生自设主题的情况下生成的类比呈现出很大的多样性，而常规类比则相对较为普遍，这突显了学生在与大型语言模型合作时的创造力的价值。学生不仅对这项活动感到兴趣，而且他们表示理解了递归，而且更容易记住与个人和文化相关的类比。

如俄罗斯套娃般”：分析计算机科学学生使用大型语言模型所生成的递归类比

"Like a Nesting Doll": Analyzing Recursion Analogies Generated by CS  Students using Large Language Models

Analogy is one of the core capacities of human cognition; when faced with new
situations, we often transfer prior experience from other domains. Most work on
computational analogy relies heavily on complex, manually crafted input. In
this work, we relax the input requirements, requiring only names of entities to
be mapped. We automatically extract commonsense representations and use them to
identify a mapping between the entities. Unlike previous works, our framework
can handle partial analogies and suggest new entities to be added. Moreover,
our method's output is easily interpretable, allowing for users to understand
why a specific mapping was chosen.
Experiments show that our model correctly maps 81.2% of classical 2x2 analogy
problems (guess level=50%). On larger problems, it achieves 77.8% accuracy
(mean guess level=13.1%). In another experiment, we show our algorithm
outperforms human performance, and the automatic suggestions of new entities
resemble those suggested by humans. We hope this work will advance
computational analogy by paving the way to more flexible, realistic input
requirements, with broader applicability.

人类认知的核心能力之一是类比；在面对新的情境时，我们经常将先前的经验从其他领域转化过来。多数关于计算类比的研究主要依赖于复杂的、手工制作的输入。本研究通过减少输入要求，只需要对实体进行映射，自动提取常识表示并将其用于实体之间的映射。与以往的工作不同，我们的框架能够处理部分类比并提出新的实体添加。此外，我们的方法的输出易于解释，允许用户理解为什么选择了特定的映射。实验证明，我们的模型能够正确映射 81.2% 的典型的 2x2 类比问题（猜测水平 = 50%）。在更大的问题上，它的准确率达到 77.8%（平均猜测水平 = 13.1%）。在另一个实验中，我们展示了我们的算法优于人类表现，并且自动提出的新实体与人类的建议类似。我们希望这项工作能通过为更灵活、更现实的输入要求铺平道路来推动计算类比的发展。

FAME: 灵活、可扩展的类比映射引擎

FAME: Flexible, Scalable Analogy Mappings Engine

Numerical perspectives help people understand extreme and unfamiliar numbers
(e.g., \$330 billion is about \$1,000 per person in the United States). While
research shows perspectives to be helpful, generating them at scale is
challenging both because it is difficult to identify what makes some analogies
more helpful than others, and because what is most helpful can vary based on
the context in which a given number appears. Here we present and compare three
policies for large-scale perspective generation: a rule-based approach, a
crowdsourced system, and a model that uses Wikipedia data and semantic
similarity (via BERT embeddings) to generate context-specific perspectives. We
find that the combination of these three approaches dominates any single
method, with different approaches excelling in different settings and users
displaying heterogeneous preferences across approaches. We conclude by
discussing our deployment of perspectives in a widely-used online word
processor.

数值观点、规模、类比、特定背景数值观点和在线字处理器。

比较可伸缩的生成数值角度策略

Comparing scalable strategies for generating numerical perspectives

Analogy is core to human cognition. It allows us to solve problems based on
prior experience, it governs the way we conceptualize new information, and it
even influences our visual perception. The importance of analogy to humans has
made it an active area of research in the broader field of artificial
intelligence, resulting in data-efficient models that learn and reason in
human-like ways. While cognitive perspectives of analogy and deep learning have
generally been studied independently of one another, the integration of the two
lines of research is a promising step towards more robust and efficient
learning techniques. As part of a growing body of research on such an
integration, we introduce the Analogical Matching Network: a neural
architecture that learns to produce analogies between structured, symbolic
representations that are largely consistent with the principles of
Structure-Mapping Theory.

该研究介绍了一种基于结构映射理论的神经网络结构 —— 模拟匹配网络（Analogical Matching Network），通过融合类比与深度学习，产生了一种人工智能模型，能够有效地学习和推理，解决新问题。