We introduce SMUTF, a unique approach for large-scale tabular data schema matching (SM), which assumes that supervised learning does not affect performance in open-domain tasks, thereby enabling effective cross-domain matching. This system uniquely combines rule-based feature engineering, pre-trained language models, and generative large language models. In an innovative adaptation inspired by the Humanitarian Exchange Language, we deploy 'generative tags' for each data column, enhancing the effectiveness of SM. SMUTF exhibits extensive versatility, working seamlessly with any pre-existing pre-trained embeddings, classification methods, and generative models. Recognizing the lack of extensive, publicly available datasets for SM, we have created and open-sourced the HDXSM dataset from the public humanitarian data. We believe this to be the most exhaustive SM dataset currently available. In evaluations across various public datasets and the novel HDXSM dataset, SMUTF demonstrated exceptional performance, surpassing existing state-of-the-art models in terms of accuracy and efficiency, and} improving the F1 score by 11.84% and the AUC of ROC by 5.08%.

翻译：我们提出了SMUTF，一种面向大规模表格数据模式匹配（SM）的创新方法。该方法假设监督学习不影响开放领域任务的性能，从而有效实现跨领域模式匹配。该体系创新性地融合了基于规则的特征工程、预训练语言模型和生成式大语言模型。受《人道主义交换语言》启发，我们创新性地为每个数据列部署了"生成式标签"，显著提升了模式匹配的效果。SMUTF展现出广泛兼容性，可与任意现有预训练嵌入、分类方法和生成模型无缝协作。针对公开可用的大型模式匹配数据集匮乏问题，我们基于公开人道主义数据创建并开源了HDXSM数据集，我们认为这是目前最完备的模式匹配数据集。在多个公开数据集及新构建的HDXSM数据集上的评估表明，SMUTF在准确率和效率方面均超越现有最优模型，F1分数提升11.84%，ROC曲线下面积提升5.08%。