In-context Clustering-based Entity Resolution with Large Language Models: A Design Space Exploration

Summary: In-context clustering: prompt LLMs to cluster record sets directly (vs pairwise), reducing API calls/time and improving scalability. LLM-CER maps the design space (set size, diversity, ordering), adds cluster-merging and hallucination mitigation, yielding up to 150% accuracy gains and 5× fewer API calls. (summarized by gpt-5-mini on Feb 11 2026)

Paper ID: h24c877770cf78063
Venue: SIGMOD
Year: 2026
Pagerank: 4.9769913e-05
Overall Rank: 10,538 | 29.18%
DOI: 10.1145/3749170

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

1. Jiajie Fu (Zhejiang University)
2. Haitong Tang (Zhejiang University)
3. Arijit Khan (Aalborg University; Bowling Green State University)
4. Sharad Mehrotra (University of California Irvine)
5. Xiangyu Ke (Zhejiang University)
6. Yunjun Gao (Zhejiang University)

BibTeX Citation

@inproceedings{fu_sigmod26,
        title = {{In-context Clustering-based Entity Resolution with Large Language Models: A Design Space Exploration}},
        author = {Fu, Jiajie and Tang, Haitong and Khan, Arijit and Mehrotra, Sharad and Ke, Xiangyu and Gao, Yunjun},
        series = {{SIGMOD} '26},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3749170},
        url = {https://dl.acm.org/doi/10.1145/3749170},
        year = {2026}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
6,342	VikingMem: A Memory Base Management System for Stateful LLM-based Applications	2026	VLDB	5.8064898e-05
11,013	LLM-CER: An Interactive System for In-context Clustering-based Entity Resolution with Large Language Models	2026	VLDB	4.9769913e-05

Outgoing Citations (Sorted by Pagerank)

Showing 20 of 20 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
20	Similarity Search in High Dimensions via Hashing	1999	VLDB	0.0005758001
158	Deep Learning for Entity Matching: A Design Space Exploration	2018	SIGMOD	0.00028038831
198	CrowdER: Crowdsourcing Entity Resolution	2012	VLDB	0.00025546182
329	Can Foundation Models Wrangle Your Data?	2023	VLDB	0.00020867521
433	Corleone: Hands-Off Crowdsourcing for Entity Matching	2014	SIGMOD	0.00018324965
457	Distributed Representations of Tuples for Entity Resolution	2018	VLDB	0.00017899824
530	Magellan: Toward Building Entity Matching Management Systems	2016	VLDB	0.00016847532
623	Entity Resolution: Theory, Practice & Open Challenges	2012	VLDB	0.00015477107
871	Leveraging Transitive Relations for Crowdsourced Joins	2013	SIGMOD	0.00013338722
885	Framework for Evaluating Clustering Algorithms in Duplicate Detection	2009	VLDB	0.00013254509
888	Pay-as-you-go User Feedback for Dataspace Systems	2008	SIGMOD	0.00013250185
934	Question Selection for Crowd Entity Resolution	2013	VLDB	0.00012998402
1,472	Crowdsourcing Algorithms for Entity Resolution	2014	VLDB	0.00010553304
1,655	Falcon: Scaling Up Hands-Off Crowdsourced Entity Matching to Build Cloud Services	2017	SIGMOD	9.9694129e-05
2,111	Comparative Analysis of Approximate Blocking Techniques for Entity Resolution	2016	VLDB	9.0194027e-05
2,475	A Comprehensive Benchmark Framework for Active Learning Methods in Entity Matching	2020	SIGMOD	8.4074448e-05
2,514	Deep Learning for Blocking in Entity Matching: A Design Space Exploration	2021	VLDB	8.3610263e-05
4,035	Dual-Objective Fine-Tuning of BERT for Entity Matching	2021	VLDB	6.8363535e-05
4,053	Crowd-Based Deduplication: An Adaptive Approach	2015	SIGMOD	6.8253348e-05
4,625	Cost-Effective Crowdsourced Entity Resolution: A Partial-Order Approach	2016	SIGMOD	6.4948389e-05

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