Leveraging Large Language Models for Entity Matching

• URL: http://arxiv.org/abs/2405.20624v1
• Date: Fri, 31 May 2024 05:22:07 GMT
• Title: Leveraging Large Language Models for Entity Matching
• Authors: Qianyu Huang, Tongfang Zhao,
• Abstract summary: This vision paper explores the application of Large Language Models (LLMs) to entity matching (EM) LLMs offer transformative potential for EM, leveraging their advanced semantic understanding and contextual capabilities. We review related work on applying weak supervision and unsupervised approaches to EM, highlighting how LLMs can enhance these methods.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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