The ability of Large Language Models (LLMs) to encode syntactic and semantic structures of language is well examined in NLP. Additionally, analogy identification, in the form of word analogies are extensively studied in the last decade of language modeling literature. In this work we specifically look at how LLMs' abilities to capture sentence analogies (sentences that convey analogous meaning to each other) vary with LLMs' abilities to encode syntactic and semantic structures of sentences. Through our analysis, we find that LLMs' ability to identify sentence analogies is positively correlated with their ability to encode syntactic and semantic structures of sentences. Specifically, we find that the LLMs which capture syntactic structures better, also have higher abilities in identifying sentence analogies.

翻译：大型语言模型（LLMs）对语言句法结构和语义结构的编码能力在自然语言处理领域已得到充分研究。此外，以词类比为形式的类比识别在过去十年的语言建模文献中也得到了广泛探讨。本研究专门考察了LLMs捕获句子类比（即传达彼此类比意义的句子）的能力如何随其编码句子句法结构和语义结构的能力而变化。通过分析，我们发现LLMs识别句子类比的能力与其编码句子句法结构和语义结构的能力呈正相关。具体而言，句法结构捕获能力更强的LLMs在识别句子类比方面也展现出更高的能力。