# Labeling Training Data for Entity Matching Using Large Language Models

arXiv: https://arxiv.org/abs/2606.28823 · HTML: https://mlsanity.com/paper/2606.28823/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Knowledge distillation for LLMs, one of 12 directions in Knowledge distillation.
- Builds on 22 of its 50 references that are in the map.
- First posted on arXiv 27 Jun 2026.

## Placed in

- Knowledge distillation / [Knowledge distillation for LLMs](https://mlsanity.com/distill/knowledge-distillation-for-llms/)

## Builds on (most cited first, 22 of 22)

- [ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks](https://mlsanity.com/paper/2303.15056/) (2023)
- [Confident Learning: Estimating Uncertainty in Dataset Labels](https://mlsanity.com/paper/1911.00068/) (2019)
- [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)
- [CoAnnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data Annotation](https://mlsanity.com/paper/2310.15638/) (2023)
- [Low-resource Deep Entity Resolution with Transfer and Active Learning](https://mlsanity.com/paper/1906.08042/) (2019)
- [Neural Networks for Entity Matching: A Survey](https://mlsanity.com/paper/2010.11075/) (2020)
- [Large Language Models as Annotators: Enhancing Generalization of NLP Models at Minimal Cost](https://mlsanity.com/paper/2306.15766/) (2023)
- [Match, Compare, or Select? An Investigation of Large Language Models for Entity Matching](https://mlsanity.com/paper/2405.16884/) (2024)
- [Supervised Contrastive Learning for Product Matching](https://mlsanity.com/paper/2202.02098/) (2022)
- [The Effects of Data Quality on Machine Learning Performance on Tabular Data](https://mlsanity.com/paper/2207.14529/) (2022)
- [Machamp: A Generalized Entity Matching Benchmark](https://mlsanity.com/paper/2106.08455/) (2021)
- [Fine-tuning Large Language Models for Entity Matching](https://mlsanity.com/paper/2409.08185/) (2024)
- [Bridging the Gap between Reality and Ideality of Entity Matching: A Revisiting and Benchmark Re-Construction](https://mlsanity.com/paper/2205.05889/) (2022)
- [Deep Indexed Active Learning for Matching Heterogeneous Entity Representations](https://mlsanity.com/paper/2104.03986/) (2021)
- [Are Chatbots Reliable Text Annotators? Sometimes](https://mlsanity.com/paper/2311.05769/) (2023)
- [Learning from Natural Language Explanations for Generalizable Entity Matching](https://mlsanity.com/paper/2406.09330/) (2024)
- [The Battleship Approach to the Low Resource Entity Matching Problem](https://mlsanity.com/paper/2311.15685/) (2023)
- [Towards Better Entity Linking with Multi-View Enhanced Distillation](https://mlsanity.com/paper/2305.17371/) (2023)
- [BEACON: Budget-Aware Entity Matching Across Domains (Extended Technical Report)](https://mlsanity.com/paper/2603.11391/) (2026)
- [Progressive Entity Resolution: A Design Space Exploration](https://mlsanity.com/paper/2503.08298/) (2025)
- [3dSAGER: Geospatial Entity Resolution over 3D Objects (Technical Report)](https://mlsanity.com/paper/2511.06300/) (2025)

## Built on by (most cited first, 1 of 1)

- [Curating Merchant-Matching Training Data with Two Confidence-Gated Local LLM Judges](https://mlsanity.com/paper/2609.33878/) (2026)

## Abstract (arXiv)

Large language models (LLMs) achieve strong entity matching performance without task-specific training data, but applying them to large sets of candidate pairs is slow and costly. Matchers built on pretrained language models (PLMs), such as BERT, offer faster inference but require training data. We systematically study knowledge-distillation workflows in which an LLM teacher labels training pairs for a smaller student matcher. We vary pair selection, labeling budget, teacher model, correspondence post-processing, and student model across eight benchmarks, including unseen entities and non-English data. We compare students trained on machine-labeled data with matchers trained on the original benchmark training sets. In most cases, PLM-based matchers trained on LLM-labeled data perform similarly to those trained on benchmark sets. Pair selection matters most for small labeling budgets, where active learning is often most effective. An open-weight teacher trains competitive students, so distillation requires no closed-weight models. Compact PLM-based students compete with much larger LLM students on most tasks while requiring 34 to 459 times less inference time than direct LLM matching. On the two benchmarks with high shares of unseen products, PLM-based students substantially underperform their teachers, as do students trained on benchmark data. Under GPT-5.2 pricing, LLM labeling costs per training set average 5.86 to 8.11. These findings support knowledge distillation as a practical approach to reduce the effort of labeling task-specific training data while enabling efficient inference.