Assessing GPT's Potential for Word Sense Disambiguation: A Quantitative Evaluation on Prompt Engineering Techniques

Modern digital communications (including social media content) often contain ambiguous words due to their potential for multiple related interpretations (polysemy). This ambiguity poses challenges for traditional Word Sense Disambiguation (WSD) methods, which struggle with limited data and lack of contextual understanding. These limitations hinder efficient translation, information retrieval, and question-answering systems, thereby restricting the benefits of computational linguistics techniques when applied to digital communication technologies. Our research investigates the use of Large Language Models (LLMs) to improve WSD using various prompt engineering techniques. We propose and evaluate a novel method that combines a knowledge graph, together with Part-of-Speech (POS) tagging and few-shot prompting to guide LLMs. By utilizing prompt augmentation with human-in-loop on few-shot prompt approaches, this work demonstrates a substantial improvement in WSD. This research advances accurate word interpretation in digital communications, leading to important implications for improved translation systems, better search results, and more intelligent question-answering technology.