SMUTF：使用生成式标签与混合特征进行模式匹配
SMUTF: Schema Matching Using Generative Tags and Hybrid Features
AI总结：
本文提出 SMUTF 模式匹配方法，结合规则特征工程、预训练语言模型和生成式大语言模型，并引入生成式标签，同时开源 HDXSM 数据集，在多个数据集上显著超越现有最优模型。
摘要：
我们提出了 SMUTF（使用生成式标签与混合特征进行模式匹配），这是一种用于大规模表格数据模式匹配（SM）的独特方法。该方法假设监督学习不会影响开放域任务中的性能，从而能够实现有效的跨域匹配。该系统独特地结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。在受人道主义交换语言启发的创新性适配中，我们为每个数据列部署“生成式标签”，从而增强模式匹配的有效性。SMUTF 具有广泛的通用性，可与任何已有的预训练嵌入、分类方法和生成模型无缝协作。认识到模式匹配领域缺乏广泛且公开可用的数据集，我们基于公开的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详尽的模式匹配数据集。在多个公开数据集和新提出的 HDXSM 数据集上的评估中，SMUTF 表现出卓越的性能，在准确性和效率方面超越了现有的最先进模型，将 F1 分数提高了 11.84%，将 ROC 的 AUC 提高了 5.08%。代码可在 https://github.com/fireindark707/Python-Schema-Matching 获取。
英文摘要：
We introduce SMUTF (Schema Matching Using Generative Tags and Hybrid Features), 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%. Code is available at https://github.com/fireindark707/Python-Schema-Matching.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
