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EMNLP2025Top-tier venue

MADAWSD: Multi-Agent Debate Framework for Adversarial Word Sense Disambiguation

Kaiyuan Zhang, Qian Liu, Luyang Zhang, Chaoqun Zheng, Shuaimin Li, Bing Xu, Muyun Yang, Xinxiao Qiao, Wenpeng Lu

2025Year
1Top-tier citations

Abstract

Word sense disambiguation (WSD) is a fundamental yet challenging task in natural language processing. In recent years, the advent of large language models (LLMs) has led to significant advancements in regular WSD tasks. However, most existing LLMs face two major issues that hinder their performance in WSD. Firstly, these models are often prone to misclassifying the correct meaning of an ambiguous word when confronted with contexts containing adversarial information. Secondly, there is a lack of sufficient adversarial WSD datasets, which severely limits the development and evaluation of adversarial WSD systems. To address these gaps, we propose a novel Multi-Agent Debate framework for Adversarial Word Sense Disambiguation (MADAWSD). The MADAWSD framework simulates a real-world debate environment where multiple agent roles, namely, the Debater, Moderator, Consensus-seeker, and Judge, engage in discussions about ambiguous words in the context of adversarial information. Through a collaborative mechanism among these agents, it achieves accurate WSD. Additionally, a novel dataset for Chinese adversarial WSD has been constructed, focusing on improving and evaluating the performance of WSD models in the Chinese language. Extensive experiments on both English and Chinese adversarial WSD datasets demonstrate that MADAWSD can seamlessly integrate with existing LLMs and significantly enhance their performance, showcasing broad generality and outstanding effectiveness. 1 * Corresponding author 1 The full source code and datasets are publicly available at https://github.com/KaiyCheung/MADAWSD . Context: Apple 's employees are eating fruits. Debate Topic: In this sentence: 'Apple 's employees are eating fruits.', classify the occurrence of the word 'apple' for "fruit" or for "company". Output the reason before output the answer. For example: Reason: Give your reasons. Answer: "fruit " or "company" Reason: The word …, So I think … Answer: fruit Reason: I disagree your opinion… because ….. So …..

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