Leveraging Large Language Models for Entity Matching
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914817594359808 |
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| author | Huang, Qianyu Zhao, Tongfang |
| author_facet | Huang, Qianyu Zhao, Tongfang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_20624 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Leveraging Large Language Models for Entity Matching Huang, Qianyu Zhao, Tongfang Computation and Language Artificial Intelligence 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. |
| title | Leveraging Large Language Models for Entity Matching |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2405.20624 |