On the Relationship between Sentence Analogy Identification and Sentence Structure Encoding in Large Language Models

• URL: http://arxiv.org/abs/2310.07818v3
• Date: Tue, 6 Feb 2024 02:24:53 GMT
• Title: On the Relationship between Sentence Analogy Identification and Sentence Structure Encoding in Large Language Models
• Authors: Thilini Wijesiriwardene, Ruwan Wickramarachchi, Aishwarya Naresh Reganti, Vinija Jain, Aman Chadha, Amit Sheth, Amitava Das
• Abstract summary: We look at how Large Language Models' abilities to capture sentence analogies vary with their abilities to encode syntactic and semantic structures. We find that the LLMs which capture syntactic structures better, also have higher abilities in identifying sentence analogies.
• Score: 7.716762867270514
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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