# Exploring the Word Sense Disambiguation Capabilities of Large Language Models

arXiv: https://arxiv.org/abs/2503.08662 · HTML: https://mlsanity.com/paper/2503.08662/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 11 Mar 2025.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Qwen2 Technical Report](https://mlsanity.com/paper/2407.10671/) (2024)
- [ZeRO: Memory Optimizations Toward Training Trillion Parameter Models](https://mlsanity.com/paper/1910.02054/) (2019)
- [Large Language Models and Multimodal Retrieval for Visual Word Sense Disambiguation](https://mlsanity.com/paper/2310.14025/) (2023)
- [ARPA: A Novel Hybrid Model for Advancing Visual Word Disambiguation Using Large Language Models and Transformers](https://mlsanity.com/paper/2408.06040/) (2024)

## Built on by (most cited first, 10 of 10)

- [Do Large Language Models Understand Word Senses?](https://mlsanity.com/paper/2509.13905/) (2025)
- [An Exploration-Analysis-Disambiguation Reasoning Framework for Word Sense Disambiguation with Low-Parameter LLMs](https://mlsanity.com/paper/2603.05400/) (2026)
- [Pragmatics beyond humans: meaning, communication, and LLMs](https://mlsanity.com/paper/2508.06167/) (2025)
- [Creating a Hybrid Rule and Neural Network Based Semantic Tagger using Silver Standard Data: the PyMUSAS framework for Multilingual Semantic Annotation](https://mlsanity.com/paper/2601.09648/) (2026)
- [SwanNLP at SemEval-2026 Task 5: An LLM-based Framework for Plausibility Scoring in Narrative Word Sense Disambiguation](https://mlsanity.com/paper/2604.16262/) (2026)
- [Mimir: Large-scale Multilingual Concept Modeling](https://mlsanity.com/paper/2605.25263/) (2026)
- [Inspicio: Open-Vocabulary, LLM-Based Sense Retrieval for Historical Languages](https://mlsanity.com/paper/2609.00998/) (2026)
- [Technical Manual for a Toolkit for Measuring Contextual Individuation in Transformer Language Models](https://mlsanity.com/paper/2609.05333/) (2026)
- [English Word Sense Disambiguation in 2026: When the Labels Become the Bottleneck](https://mlsanity.com/paper/2609.17554/) (2026)
- [WiC is Not WSD: A Study on LLMs and Lexical Ambiguity Resolution](https://mlsanity.com/paper/2609.20593/) (2026)

## Abstract (arXiv)

Word Sense Disambiguation (WSD) is a historical task in computational linguistics that has received much attention over the years. However, with the advent of Large Language Models (LLMs), interest in this task (in its classical definition) has decreased. In this study, we evaluate the performance of various LLMs on the WSD task. We extend a previous benchmark (XL-WSD) to re-design two subtasks suitable for LLM: 1) given a word in a sentence, the LLM must generate the correct definition; 2) given a word in a sentence and a set of predefined meanings, the LLM must select the correct one. The extended benchmark is built using the XL-WSD and BabelNet. The results indicate that LLMs perform well in zero-shot learning but cannot surpass current state-of-the-art methods. However, a fine-tuned model with a medium number of parameters outperforms all other models, including the state-of-the-art.