# PromptEM: Prompt-tuning for Low-resource Generalized Entity Matching

arXiv: https://arxiv.org/abs/2207.04802 · HTML: https://mlsanity.com/paper/2207.04802/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2022.

## Built on by (most cited first, 10 of 10)

- [Entity Matching using Large Language Models](https://mlsanity.com/paper/2310.11244/) (2023)
- [Unsupervised Entity Alignment for Temporal Knowledge Graphs](https://mlsanity.com/paper/2302.00796/) (2023)
- [UniDM: A Unified Framework for Data Manipulation with Large Language Models](https://mlsanity.com/paper/2405.06510/) (2024)
- [MultiEM: Efficient and Effective Unsupervised Multi-Table Entity Matching](https://mlsanity.com/paper/2308.01927/) (2023)
- [Prompt-tuning with Attribute Guidance for Low-resource Entity Matching](https://mlsanity.com/paper/2603.19321/) (2026)
- [3dSAGER: Geospatial Entity Resolution over 3D Objects (Technical Report)](https://mlsanity.com/paper/2511.06300/) (2025)
- [Unlocking the Power of Large Language Models for Multi-table Entity Matching](https://mlsanity.com/paper/2604.21238/) (2026)
- [AdapterEM: Pre-trained Language Model Adaptation for Generalized Entity Matching using Adapter-tuning](https://mlsanity.com/paper/2305.18725/) (2023)
- [Topic-DPR: Topic-based Prompts for Dense Passage Retrieval](https://mlsanity.com/paper/2310.06626/) (2023)
- [Accelerating Cross-Model LLM Inference via Attention Map Reuse](https://mlsanity.com/paper/2610.14688/) (2026)

## Abstract (arXiv)

Entity Matching (EM), which aims to identify whether two entity records from two relational tables refer to the same real-world entity, is one of the fundamental problems in data management. Traditional EM assumes that two tables are homogeneous with the aligned schema, while it is common that entity records of different formats (e.g., relational, semi-structured, or textual types) involve in practical scenarios. It is not practical to unify their schemas due to the different formats. To support EM on format-different entity records, Generalized Entity Matching (GEM) has been proposed and gained much attention recently. To do GEM, existing methods typically perform in a supervised learning way, which relies on a large amount of high-quality labeled examples. However, the labeling process is extremely labor-intensive, and frustrates the use of GEM. Low-resource GEM, i.e., GEM that only requires a small number of labeled examples, becomes an urgent need. To this end, this paper, for the first time, focuses on the low-resource GEM and proposes a novel low-resource GEM method, termed as PromptEM. PromptEM has addressed three challenging issues (i.e., designing GEM-specific prompt-tuning, improving pseudo-labels quality, and running efficient self-training) in low-resource GEM. Extensive experimental results on eight real benchmarks demonstrate the superiority of PromptEM in terms of effectiveness and efficiency.