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arXiv · 2406.11255

Liberal Entity Matching as a Compound AI Toolchain

Silvery D. Fu·David Wang·Wen Zhang·Kathleen Ge
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Abstract

Entity matching (EM), the task of identifying whether two descriptions refer to the same entity, is essential in data management. Traditional methods have evolved from rule-based to AI-driven approaches, yet current techniques using large language models (LLMs) often fall short due to their reliance on static knowledge and rigid, predefined prompts. In this paper, we introduce Libem, a compound AI system designed to address these limitations by incorporating a flexible, tool-oriented approach. Libem supports entity matching through dynamic tool use, self-refinement, and optimization, allowing it to adapt and refine its process based on the dataset and performance metrics. Unlike traditional solo-AI EM systems, which often suffer from a lack of modularity that hinders iterative design improvements and system optimization, Libem offers a composable and reusable toolchain. This approach aims to contribute to ongoing discussions and developments in AI-driven data management.

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Silvery D. Fu, David Wang, Wen Zhang, Kathleen Ge. 2024-06-17. Liberal Entity Matching as a Compound AI Toolchain. https://arxiv.org/abs/2406.11255

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