Liberal Entity Matching as a Compound AI Toolchain
<|reference_start|>Liberal Entity Matching as a Compound AI Toolchain: 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.<|reference_end|>
@article{fu2024liberal,
    title={Liberal Entity Matching as a Compound AI Toolchain},
    author={Silvery D. Fu, David Wang, Wen Zhang, Kathleen Ge},
    journal={arXiv preprint arXiv:2406.11255},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.11255},
    primaryClass={cs.DB cs.AI cs.SE}
}