Do Large Language Models Understand Word Senses?

• URL: http://arxiv.org/abs/2509.13905v1
• Date: Wed, 17 Sep 2025 11:11:27 GMT
• Title: Do Large Language Models Understand Word Senses?
• Authors: Domenico Meconi, Simone Stirpe, Federico Martelli, Leonardo Lavalle, Roberto Navigli,
• Abstract summary: We evaluate the Word Sense Disambiguation (WSD) capabilities of instruction-tuned Large Language Models (LLMs)<n>We find that leading models such as GPT-4o and DeepSeek-V3 achieve performance on par with specialized WSD systems.<n>Results reveal that LLMs can explain the meaning of words in context up to 98% accuracy.
• Score: 30.57967629882536
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Understanding the meaning of words in context is a fundamental capability for Large Language Models (LLMs). Despite extensive evaluation efforts, the extent to which LLMs show evidence that they truly grasp word senses remains underexplored. In this paper, we address this gap by evaluating both i) the Word Sense Disambiguation (WSD) capabilities of instruction-tuned LLMs, comparing their performance to state-of-the-art systems specifically designed for the task, and ii) the ability of two top-performing open- and closed-source LLMs to understand word senses in three generative settings: definition generation, free-form explanation, and example generation. Notably, we find that, in the WSD task, leading models such as GPT-4o and DeepSeek-V3 achieve performance on par with specialized WSD systems, while also demonstrating greater robustness across domains and levels of difficulty. In the generation tasks, results reveal that LLMs can explain the meaning of words in context up to 98\% accuracy, with the highest performance observed in the free-form explanation task, which best aligns with their generative capabilities.

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