用大模型提升词语歧义消解，效果显著。
Can LLMs assist with Ambiguity? A Quantitative Evaluation of various Large Language Models on Word Sense Disambiguation
- 结合提示增强与知识库，引导大模型精准理解词义
- 在FEWS数据集上准确率明显提升，验证方法有效性
- 适合自然语言处理、社交媒体分析等场景使用
现代数字通信中常出现词汇歧义问题，传统词义消解（WSD）方法因数据有限而效率受限，影响翻译、信息检索和问答系统的性能。本文提出一种新方法，融合系统化提示增强机制与包含多种词义解释的知识库（KB），通过人工参与的提示增强流程，结合词性标注（POS）、歧义词同义词、基于方面的感觉筛选及少样本提示，引导大模型进行推理。采用少样本链式思维（COT）提示策略，实验在FEWS测试数据集上取得显著性能提升。研究推动了社交媒体与数字通信中的精准词义理解。
原文摘要 · Abstract (English)
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.
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