# Machamp: A Generalized Entity Matching Benchmark

arXiv: https://arxiv.org/abs/2106.08455 · HTML: https://mlsanity.com/paper/2106.08455/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2021.

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

- [Entity Matching using Large Language Models](https://mlsanity.com/paper/2310.11244/) (2023)
- [WDC Products: A Multi-Dimensional Entity Matching Benchmark](https://mlsanity.com/paper/2301.09521/) (2023)
- [Fine-tuning Large Language Models for Entity Matching](https://mlsanity.com/paper/2409.08185/) (2024)
- [Evaluating Bias in LLMs for Job-Resume Matching: Gender, Race, and Education](https://mlsanity.com/paper/2503.19182/) (2025)
- [Bridging the Gap between Reality and Ideality of Entity Matching: A Revisiting and Benchmark Re-Construction](https://mlsanity.com/paper/2205.05889/) (2022)
- [Through the Fairness Lens: Experimental Analysis and Evaluation of Entity Matching](https://mlsanity.com/paper/2307.02726/) (2023)
- [FlexER: Flexible Entity Resolution for Multiple Intents](https://mlsanity.com/paper/2209.07569/) (2022)
- [Interactive Data Harmonization with LLM Agents: Opportunities and Challenges](https://mlsanity.com/paper/2502.07132/) (2025)
- [MultiEM: Efficient and Effective Unsupervised Multi-Table Entity Matching](https://mlsanity.com/paper/2308.01927/) (2023)
- [Towards Universal Dense Blocking for Entity Resolution](https://mlsanity.com/paper/2404.14831/) (2024)
- [Labeling Training Data for Entity Matching Using Large Language Models](https://mlsanity.com/paper/2606.28823/) (2026)
- [AdapterEM: Pre-trained Language Model Adaptation for Generalized Entity Matching using Adapter-tuning](https://mlsanity.com/paper/2305.18725/) (2023)
- [APrompt4EM: Augmented Prompt Tuning for Generalized Entity Matching](https://mlsanity.com/paper/2405.04820/) (2024)
- [Beyond Scale and Generation: Understanding Language Model-based Entity Matching](https://mlsanity.com/paper/2607.24688/) (2026)
- [Corporate-Family Resolution Is Not a String-Matching Problem: A Public Benchmark Stratified by Name Visibility](https://mlsanity.com/paper/2609.04269/) (2026)
- [Billiger.de Products: A Bilingual Entity Matching Benchmark](https://mlsanity.com/paper/2609.37713/) (2026)

## Abstract (arXiv)

Entity Matching (EM) refers to the problem of determining whether two different data representations refer to the same real-world entity. It has been a long-standing interest of the data management community and many efforts have been paid in creating benchmark tasks as well as in developing advanced matching techniques. However, existing benchmark tasks for EM are limited to the case where the two data collections of entities are structured tables with the same schema. Meanwhile, the data collections for matching could be structured, semi-structured, or unstructured in real-world scenarios of data science. In this paper, we come up with a new research problem -- Generalized Entity Matching to satisfy this requirement and create a benchmark Machamp for it. Machamp consists of seven tasks having diverse characteristics and thus provides good coverage of use cases in real applications. We summarize existing EM benchmark tasks for structured tables and conduct a series of processing and cleaning efforts to transform them into matching tasks between tables with different structures. Based on that, we further conduct comprehensive profiling of the proposed benchmark tasks and evaluate popular entity matching approaches on them. With the help of Machamp, it is the first time that researchers can evaluate EM techniques between data collections with different structures.