  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.
