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English(EN) LakeHopper: Knowledge-Aware Adaptation of Column Type Annotators across Data Lakes

LakeHopper 以最小数据量实现跨数据湖的列类型标注器自适应

研究人员开发了 LakeHopper,一个旨在跨不同数据湖自适应列类型标注器的新颖系统。与之前需要对新数据集进行广泛重新训练的方法不同,LakeHopper 将跨湖自适应视为一个知识管理问题。它识别并管理特定于源、共享和特定于目标的知识,以有效地迁移标注,从而在最小的新数据量下实现显著的性能提升。 AI

影响 LakeHopper 在不同数据集之间进行高效自适应的方法,可以显著降低在多样化数据环境中部署 AI 模型的成本和时间。

排序理由 这是一篇详细介绍 AI 模型特定任务自适应新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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LakeHopper 以最小数据量实现跨数据湖的列类型标注器自适应

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这是一篇详细介绍 AI 模型特定任务自适应新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Yushi Sun, Xujia Li, Nan Tang, Quanqing Xu, Chuanhui Yang, Lei Chen ·

    LakeHopper:跨数据湖的列类型标注器知识感知自适应

    arXiv:2602.08793v2 Announce Type: replace Abstract: Column Type Annotation (CTA), which assigns a semantic type to a table column, underpins data integration, cleaning, and search over data lakes. State-of-the-art annotators are pre-trained language models (PLMs) fine-tuned on on…