Leveraging Large Language Models for Entity Matching

TL;DR

This paper discusses how Large Language Models like GPT-4 can revolutionize entity matching in data integration by leveraging their semantic understanding, while also addressing challenges and future research directions.

Contribution

It presents a comprehensive exploration of applying LLMs to entity matching, highlighting their advantages, challenges, and potential for enhancing existing weak supervision and unsupervised methods.

Findings

LLMs can improve entity matching accuracy.

Challenges include data privacy and model interpretability.

Future research directions involve integrating LLMs with existing EM techniques.

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.