A hallmark of intelligence is the ability to use a familiar domain to make
inferences about a less familiar domain, known as analogical reasoning. In this
article, we delve into the performance of Large Language Models (LLMs) in
dealing with progressively complex analogies expressed in unstructured text. We
discuss analogies at four distinct levels of complexity: lexical analogies,
syntactic analogies, semantic analogies, and pragmatic analogies. As the
analogies become more complex, they require increasingly extensive, diverse
knowledge beyond the textual content, unlikely to be found in the lexical
co-occurrence statistics that power LLMs. To address this, we discuss the
necessity of employing Neuro-symbolic AI techniques that combine statistical
and symbolic AI, informing the representation of unstructured text to highlight
and augment relevant content, provide abstraction and guide the mapping
process. Our knowledge-informed approach maintains the efficiency of LLMs while
preserving the ability to explain analogies for pedagogical applications.

使用熟悉的领域进行类比推理是智能的标志。本文研究大型语言模型在处理逐渐复杂的结构化文本中的类比时的表现，并讨论了四个不同复杂程度的类比：词汇类比、句法类比、语义类比和语用类比。为了解决这个问题，我们提出了结合统计和符号人工智能的神经符号 AI 技术的必要性，以强调和增强相关内容，并提供抽象和指导映射过程的无结构文本表示。我们的知识驱动方法保持了大型语言模型的高效性，同时保留了解释类比的能力，适用于教学应用。