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Generalized Entity Matching with Adaptivity via Large Language Models

Summary: GLEAM tackles unlabeled generalized entity matching with an LLM-guided structure/content graph, adaptive GMM–Bayesian candidate stopping, and hierarchical domain-aware reasoning. It improves F1 by up to 25.7% while reducing LLM calls across heterogeneous datasets. (summarized by gpt-5.6-luna on Jul 26 2026)

Paper ID
7439
Venue
SIGMOD
Year
2026
Pagerank
5.093636e-05
Overall Rank
10,248 | 29.69%
DOI
10.1145/3802066

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BibTeX Citation

@inproceedings{chen_sigmod26,
        title = {{Generalized Entity Matching with Adaptivity via Large Language Models}},
        author = {Chen, Xingguang and Shi, Yimin and Xiao, Xiaokui},
        series = {{SIGMOD} '26},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3802066},
        url = {https://dl.acm.org/doi/10.1145/3802066},
        year = {2026}
}

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