Ambiguous words are often found in modern digital communications. Lexical ambiguity challenges traditional Word Sense Disambiguation (WSD) methods, due to limited data. Consequently, the efficiency of translation, information retrieval, and question-answering systems is hindered by these limitations. This study investigates the use of Large Language Models (LLMs) to improve WSD using a novel approach combining a systematic prompt augmentation mechanism with a knowledge base (KB) consisting of different sense interpretations. The proposed method incorporates a human-in-loop approach for prompt augmentation where prompt is supported by Part-of-Speech (POS) tagging, synonyms of ambiguous words, aspect-based sense filtering and few-shot prompting to guide the LLM. By utilizing a few-shot Chain of Thought (COT) prompting-based approach, this work demonstrates a substantial improvement in performance. The evaluation was conducted using FEWS test data and sense tags. This research advances accurate word interpretation in social media and digital communication.

本研究解决了现代数字通信中的词汇歧义问题，传统词义消歧方法因数据受限而难以高效实施。研究提出了一种结合系统性提示增强机制与不同意义解释知识库的新方法，结果表明利用少量示例的思维链提示策略显著提高了模型在社交媒体与数字交流中的词义准确性。

大型语言模型能否协助处理歧义？对多种大型语言模型在词义消歧方面的定量评估