LLM-CER: An Interactive System for In-context Clustering-based Entity Resolution with Large Language Models
Summary: LLM-CER reframes LLM-based entity resolution as in-context clustering rather than costly pairwise matching, improving scalability and API efficiency. Its interactive pipeline studies clustering factors and supports configurable execution, hierarchical visualization, miscluster correction, and cost/quality monitoring. (summarized by gpt-5.6-luna on Aug 28 2026)
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Authors
- 1. Haoyu Wang (Zhejiang University)
- 2. Haitong Tang (Zhejiang University)
- 3. Jiajie Fu (Zhejiang University)
- 4. Arijit Khan (Bowling Green State University)
- 5. Sharad Mehrotra (University of California Irvine)
- 6. Xiangyu Ke (Zhejiang University)
- 7. Yunjun Gao (Zhejiang University)
BibTeX Citation
@article{wang_vldb26,
title = {{LLM-CER: An Interactive System for In-context Clustering-based Entity Resolution with Large Language Models}},
author = {Wang, Haoyu and Tang, Haitong and Fu, Jiajie and Khan, Arijit and Mehrotra, Sharad and Ke, Xiangyu and Gao, Yunjun},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {12},
pages = {4746--4749},
doi = {10.14778/3827998.3828112},
url = {https://doi.org/10.14778/3827998.3828112},
year = {2026}
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Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 158 | Deep Learning for Entity Matching: A Design Space Exploration | 2018 | SIGMOD | 0.00028046388 |
| 329 | Can Foundation Models Wrangle Your Data? | 2023 | VLDB | 0.00020858443 |
| 457 | Distributed Representations of Tuples for Entity Resolution | 2018 | VLDB | 0.00017907103 |
| 10,527 | In-context Clustering-based Entity Resolution with Large Language Models: A Design Space Exploration | 2026 | SIGMOD | 4.9793485e-05 |
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