In-context Clustering-based Entity Resolution with Large Language Models: A Design Space Exploration
This is experimental HTML to improve accessibility. We invite you to report rendering errors. Use Alt+Y to toggle on accessible reporting links and Alt+Shift+Y to toggle off. Learn more about this project and help improve conversions. HTML conversions sometimes display errors due to content that did not convert correctly from the source. This paper uses the following packages that are not yet supported by the HTML conversion tool. Feedback on these issues are not necessary; they are known and are being worked on. Authors: achieve the best HTML results from your LaTeX submissions by following these best practices. Entity Resolution (ER) is a fundamental data quality improvement task that identifies and links records referring to the same real-world entity. Traditional ER approaches often rely on pairwise comparisons, which can be costly regarding both time and monetary resources, especially when large datasets are involved. Recently, Large Language Models (LLMs) have demonstrated promi
DOI: XXXXXXX.XXXXXXXConference: ACM International Conference on Management of Data; May 31–June 5, 2026; Bengaluru, IndiaPrice: 15.00ISBN: 978-1-4503-XXXX-X/18/06 , Haitong Tang Affiliation: Zhejiang University email: tht@zju.edu.cn , Arijit Khan Affiliation: Aalborg University email: arijitk@cs.aau.dk , Sharad Mehrotra Affiliation: University of California, Irvine email: sharad@ics.uci.edu , Xiangyu Ke Affiliation: Zhejiang University email: xiangyu.ke@zju.edu.cn and Yunjun Gao Affiliation: Zhejiang University email: gaoyj@zju.edu.cn © acmcopyright Abstract. Entity…
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