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

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 promising results in ER tasks. Still, existing methods typically focus on pairwise matching, missing the potential of LLMs to directly perform clustering in a more cost-effective and scalable manner. In this paper, we propose a novel in-context clustering approach for ER, where LLMs are used to cluster records directly, reducing both time complexity and monetary costs. We systematically investigate the design space for in-context clustering, analyzing the impact of factors such as set size, diversity, variation, and ordering of records on clustering performance. Based on these insights, we develop LLM-CER (LLM-powered Clustering-based ER) that obtains high-quality ER results while minimizing LLM API calls. Our approach addresses key challenges, including efficient cluster merging and LLM's hallucination, providing a scalable and effective solution for ER. Extensive experiments on nine real-world datasets demonstrate that our method significantly improves result quality, achieving up to 150% higher accuracy, 10% increase in the FP-measure, and reducing API calls by up to 5X, while maintaining a comparable monetary cost to the most cost-effective baseline.