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.

翻译：智能的标志之一是能够利用熟悉的领域对较陌生的领域进行推理，即类比推理。本文深入探讨了大语言模型在处理以非结构化文本表达的渐进复杂类比时的表现。我们讨论了四个不同复杂层次的类比：词汇类比、句法类比、语义类比和语用类比。随着类比复杂程度的增加，它们需要越来越广泛且多样化的文本内容之外的知识，而这些知识不太可能出现在驱动LLM的词汇共现统计中。为此，我们探讨了采用神经符号AI技术的必要性，该技术结合了统计AI和符号AI，能够对非结构化文本的表征进行信息增强，以突出和补充相关内容，提供抽象化并引导映射过程。我们基于知识的方法在保持LLM效率的同时，仍保留了对类比进行解释的能力，适用于教学应用场景。