On the Relationship between Sentence Analogy Identification and Sentence Structure Encoding in Large Language Models
This work addresses the understanding of language model capabilities for researchers, but it is incremental as it extends known word analogy studies to sentences.
The study investigated the relationship between large language models' ability to identify sentence analogies and their encoding of syntactic and semantic structures, finding a positive correlation where better structure encoding leads to higher analogy identification.
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.