# ReMatch: Retrieval Enhanced Schema Matching with LLMs

arXiv: https://arxiv.org/abs/2403.01567 · HTML: https://mlsanity.com/paper/2403.01567/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 34 references that are in the map.
- First posted on arXiv 3 Mar 2024.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [How to Fine-Tune BERT for Text Classification?](https://mlsanity.com/paper/1905.05583/) (2019)
- [Text and Code Embeddings by Contrastive Pre-Training](https://mlsanity.com/paper/2201.10005/) (2022)
- [Large Language Models as General Pattern Machines](https://mlsanity.com/paper/2307.04721/) (2023)
- [Can Foundation Models Wrangle Your Data?](https://mlsanity.com/paper/2205.09911/) (2022)
- [Deep Entity Matching with Pre-Trained Language Models](https://mlsanity.com/paper/2004.00584/) (2020)
- [Large Language Models as Data Preprocessors](https://mlsanity.com/paper/2308.16361/) (2023)
- [Learning to Characterize Matching Experts](https://mlsanity.com/paper/2012.01229/) (2020)

## Built on by (most cited first, 16 of 16)

- [LiveBench: A Challenging, Contamination-Limited LLM Benchmark](https://mlsanity.com/paper/2406.19314/) (2024)
- [A Survey of Data Agents: Emerging Paradigm or Overstated Hype?](https://mlsanity.com/paper/2510.23587/) (2025)
- [Knowledge Graph-based Retrieval-Augmented Generation for Schema Matching](https://mlsanity.com/paper/2501.08686/) (2025)
- [Matchmaker: Self-Improving Large Language Model Programs for Schema Matching](https://mlsanity.com/paper/2410.24105/) (2024)
- [KcMF: A Knowledge-compliant Framework for Schema and Entity Matching with Fine-tuning-free LLMs](https://mlsanity.com/paper/2410.12480/) (2024)
- [Empowering Tabular Data Preparation with Language Models: Why and How?](https://mlsanity.com/paper/2508.01556/) (2025)
- [Towards Scalable Schema Mapping using Large Language Models](https://mlsanity.com/paper/2505.24716/) (2025)
- [LLM Agents Are the Antidote to Walled Gardens](https://mlsanity.com/paper/2506.23978/) (2025)
- [Prompt-Matcher: Leveraging Large Models to Reduce Uncertainty in Schema Matching Results](https://mlsanity.com/paper/2408.14507/) (2024)
- [Mapping Smarter, Not Harder: A Test-Time Reinforcement Learning Agent That Improves Without Labels or Model Updates](https://mlsanity.com/paper/2510.14900/) (2025)
- [Towards Trustworthy and Cost-Efficient Data Integration: From Na"ive RAG to Agentic RAG](https://mlsanity.com/paper/2607.22319/) (2026)
- [Constraint-Guided Enterprise Data Mapping with Large Language Models](https://mlsanity.com/paper/2608.24218/) (2026)
- [Discovering Relationships in Data Lakes Using Large Language Models: An Industrial Case](https://mlsanity.com/paper/2608.26750/) (2026)
- [Surprising Effectiveness of Self-Demonstrations in Enhancing Schema-Ontology Mapping with LLMs](https://mlsanity.com/paper/2609.13776/) (2026)
- [REALMS: An AI-Assistant Conversational System for Real-Time Exact Audience Sizing over High-Dimensional Nested Profiles](https://mlsanity.com/paper/2609.30547/) (2026)
- [Correspondences as Decisions: JevNexus for Decision-Centric Schema Matching](https://mlsanity.com/paper/2610.09487/) (2026)

## Abstract (arXiv)

Schema matching is a crucial task in data integration, involving the alignment of a source schema with a target schema to establish correspondence between their elements. This task is challenging due to textual and semantic heterogeneity, as well as differences in schema sizes. Although machine-learning-based solutions have been explored in numerous studies, they often suffer from low accuracy, require manual mapping of the schemas for model training, or need access to source schema data which might be unavailable due to privacy concerns. In this paper we present a novel method, named ReMatch, for matching schemas using retrieval-enhanced Large Language Models (LLMs). Our method avoids the need for predefined mapping, any model training, or access to data in the source database. Our experimental results on large real-world schemas demonstrate that ReMatch is an effective matcher. By eliminating the requirement for training data, ReMatch becomes a viable solution for real-world scenarios.