Leveraging Large Language Models for Entity Matching

2024/05/31 by Qianyu Huang, Huang, Qianyu, Tongfang Zhao +1 · 1 citation
Computer Science · Decision Sciences · Health Professions · #Artificial Intelligence (cs.AI) #Artificial Intelligence in Healthcare #Computation and Language (cs.CL) #Data Quality and Management #FOS: Computer and information sciences #Topic Modeling

paper · pdf · doi:10.48550/arxiv.2405.20624

openalex publication_date 2024/05/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

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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• Beyond Traditional Algorithms: Leveraging LLMs for Accurate Cross-Border Entity Identification

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