# Leveraging Large Language Models for Entity Matching

arXiv: https://arxiv.org/abs/2405.20624 · HTML: https://mlsanity.com/paper/2405.20624/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 34 references that are in the map.
- First posted on arXiv 31 May 2024.

## Builds on (most cited first, 17 of 17)

- [Snorkel: Rapid Training Data Creation with Weak Supervision](https://mlsanity.com/paper/1711.10160/) (2017)
- [Adapting Large Language Models by Integrating Collaborative Semantics for Recommendation](https://mlsanity.com/paper/2311.09049/) (2023)
- [FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning](https://mlsanity.com/paper/2309.00363/) (2023)
- [Deep Entity Matching with Pre-Trained Language Models](https://mlsanity.com/paper/2004.00584/) (2020)
- [How to Index Item IDs for Recommendation Foundation Models](https://mlsanity.com/paper/2305.06569/) (2023)
- [Usable XAI: 10 Strategies Towards Exploiting Explainability in the LLM Era](https://mlsanity.com/paper/2403.08946/) (2024)
- [An Embarrassingly Simple Approach for Transfer Learning from Pretrained Language Models](https://mlsanity.com/paper/1902.10547/) (2019)
- [UDAPDR: Unsupervised Domain Adaptation via LLM Prompting and Distillation of Rerankers](https://mlsanity.com/paper/2303.00807/) (2023)
- [ZeroER: Entity Resolution using Zero Labeled Examples](https://mlsanity.com/paper/1908.06049/) (2019)
- [A Comprehensive Benchmark Framework for Active Learning Methods in Entity Matching](https://mlsanity.com/paper/2003.13114/) (2020)
- [Divide-or-Conquer? Which Part Should You Distill Your LLM?](https://mlsanity.com/paper/2402.15000/) (2024)
- [MetaIE: Distilling a Meta Model from LLM for All Kinds of Information Extraction Tasks](https://mlsanity.com/paper/2404.00457/) (2024)
- [Quantifying Adaptability in Pre-trained Language Models with 500 Tasks](https://mlsanity.com/paper/2112.03204/) (2021)
- [Similar Data Points Identification with LLM: A Human-in-the-loop Strategy Using Summarization and Hidden State Insights](https://mlsanity.com/paper/2404.04281/) (2024)
- [A Fast, Performant, Secure Distributed Training Framework For Large Language Model](https://mlsanity.com/paper/2401.09796/) (2024)
- [Demonstration of Panda: A Weakly Supervised Entity Matching System](https://mlsanity.com/paper/2106.10821/) (2021)
- [Ground Truth Inference for Weakly Supervised Entity Matching](https://mlsanity.com/paper/2211.06975/) (2022)

## Built on by (most cited first, 3 of 3)

- [Beyond Traditional Algorithms: Leveraging LLMs for Accurate Cross-Border Entity Identification](https://mlsanity.com/paper/2507.11086/) (2025)
- [Measuring Social Integration Through Participation: Categorizing Organizations and Leisure Activities in the Displaced Karelians Interview Archive using LLMs](https://mlsanity.com/paper/2602.15436/) (2026)
- [Data Agents: Agentic Data Systems](https://mlsanity.com/paper/2609.24137/) (2026)

## 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.