mlsanityData to 7 Oct 2026

Leveraging Large Language Models for Entity Matching

arXiv 2405.20624 ↗First posted 31 May 2024cs.CL cs.AIcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 17 of its 34 references that are in the map. First posted on arXiv 31 May 2024.

Builds on

17 of 34 references.

References · 17

  1. 2017Snorkel: Rapid Training Data Creation with Weak Supervisioncited by 370
  2. 2023Adapting Large Language Models by Integrating Collaborative Semantics for Recommendationcited by 171
  3. 2023FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learningcited by 146
  4. 2020Deep Entity Matching with Pre-Trained Language Modelscited by 143
  5. 2023How to Index Item IDs for Recommendation Foundation Modelscited by 102
  6. 2024Usable XAI: 10 Strategies Towards Exploiting Explainability in the LLM Eracited by 45
  7. 2019An Embarrassingly Simple Approach for Transfer Learning from Pretrained Language Modelscited by 39
  8. 2023UDAPDR: Unsupervised Domain Adaptation via LLM Prompting and Distillation of Rerankerscited by 37
  9. 2019ZeroER: Entity Resolution using Zero Labeled Examplescited by 28
  10. 2020A Comprehensive Benchmark Framework for Active Learning Methods in Entity Matchingcited by 17
  11. 2024Divide-or-Conquer? Which Part Should You Distill Your LLM?cited by 17
  12. 2024MetaIE: Distilling a Meta Model from LLM for All Kinds of Information Extraction Taskscited by 14
5 more
  1. 2021Quantifying Adaptability in Pre-trained Language Models with 500 Taskscited by 13
  2. 2024Similar Data Points Identification with LLM: A Human-in-the-loop Strategy Using Summarization and Hidden State Insightscited by 9
  3. 2024A Fast, Performant, Secure Distributed Training Framework For Large Language Modelcited by 8
  4. 2021Demonstration of Panda: A Weakly Supervised Entity Matching Systemcited by 6
  5. 2022Ground Truth Inference for Weakly Supervised Entity Matchingcited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Beyond Traditional Algorithms: Leveraging LLMs for Accurate Cross-Border Entity Identificationcited by 0
  2. 2026Measuring Social Integration Through Participation: Categorizing Organizations and Leisure Activities in the Displaced Karelians Interview Archive using LLMscited by 0
  3. 2026Data Agents: Agentic Data Systemscited by 0