Summary
A new paper (arXiv:2603.05400) introduces the Exploration-Analysis-Disambiguation (EAD) reasoning framework, designed to improve Word Sense Disambiguation (WSD) in NLP using low-parameter large language models. WSD remains difficult for rare or ambiguous words where context alone is insufficient, and LLMs often fail because they rely on frequent sense biases rather than genuine contextual analysis. EAD addresses this by guiding models through three explicit reasoning stages: exploration of potential senses, analysis of contextual relevance, and final disambiguation. According to the authors, experiments show the approach significantly improves WSD performance on standard benchmarks while using models with fewer than 10 billion parameters. The work is authored by Junyu Lu, Yanan Zheng, Chen Gong, Yibo Zhang, Shengnan Li, Yining Zhang, Daimeng Wei, Zhiqiang Zhang, and Jiajun Zhang.
Paper Overview
Field: NLP
Authors: Junyu Lu, Yanan Zheng, Chen Gong, Yibo Zhang, Shengnan Li, Yining Zhang, Daimeng Wei, Zhiqiang Zhang, Jiajun Zhang
Published: 2026-03-05
arXiv: 2603.05400
Abstract
Word Sense Disambiguation (WSD) remains a key challenge in Natural Language Processing (NLP), especially when dealing with rare or ambiguous words where context alone is insufficient. While large language models (LLMs) have shown promise, their performance on WSD tasks is often hindered by their tendency to rely on frequent sense biases rather than contextual analysis.
In this paper, the authors propose an Exploration-Analysis-Disambiguation (EAD) reasoning framework that enables low-parameter LLMs to perform explicit reasoning for WSD. The framework consists of three stages:
1. Exploration of potential senses
2. Analysis of contextual relevance
3. Final disambiguation
Experiments show that this approach significantly improves WSD performance on standard benchmarks while using models with fewer than 10 billion parameters.
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