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

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 lo...

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Bibliographic Details
Published in:arXiv.org 2024-02
Main Authors: Wijesiriwardene, Thilini, Wickramarachchi, Ruwan, Reganti, Aishwarya Naresh, Jain, Vinija, Chadha, Aman, Sheth, Amit, Das, Amitava
Format: Article
Language:English
Subjects:
Analogies
Cognition
Large language models
Semantics
Sentences
Online Access:Get full text
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Summary: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.
ISSN:2331-8422