Entity matching (EM) is a critical task in data integration, aiming to identify records across different datasets that refer to the same real-world entities. Traditional methods often rely on manually engineered features and rule-based systems, which struggle with diverse and unstructured data. The emergence of Large Language Models (LLMs) such as GPT-4 offers transformative potential for EM, leveraging their advanced semantic understanding and contextual capabilities. This vision paper explores the application of LLMs to EM, discussing their advantages, challenges, and future research directions. Additionally, we review related work on applying weak supervision and unsupervised approaches to EM, highlighting how LLMs can enhance these methods.

翻译：实体匹配（EM）是数据集成中的关键任务，旨在识别不同数据集中指向同一现实世界实体的记录。传统方法通常依赖人工设计的特征和基于规则的系统，这些方法在处理多样化和非结构化数据时面临困难。以GPT-4为代表的大型语言模型（LLMs）的出现为实体匹配带来了变革性潜力，其先进的语义理解和上下文处理能力可被有效利用。本愿景论文探讨了LLMs在实体匹配中的应用，分析了其优势、挑战及未来研究方向。此外，我们回顾了将弱监督和无监督方法应用于实体匹配的相关工作，重点阐述了LLMs如何增强这些方法的效能。