LakeHopper: Cross Data Lakes Column Type Annotation through Model Adaptation

• URL: http://arxiv.org/abs/2602.08793v1
• Date: Mon, 09 Feb 2026 15:30:07 GMT
• Title: LakeHopper: Cross Data Lakes Column Type Annotation through Model Adaptation
• Authors: Yushi Sun, Xujia Li, Nan Tang, Quanqing Xu, Chuanhui Yang, Lei Chen,
• Abstract summary: Column type annotation is vital for tasks like data cleaning, integration, and visualization.<n>Recent solutions rely on resource-intensive language models fine-tuned on well-annotated columns from a particular set of tables.<n>We propose LakeHopper, a framework that identifies and resolves the knowledge gap through LM interactions.
• Score: 18.72484471043965
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Column type annotation is vital for tasks like data cleaning, integration, and visualization. Recent solutions rely on resource-intensive language models fine-tuned on well-annotated columns from a particular set of tables, i.e., a source data lake. In this paper, we study whether we can adapt an existing pre-trained LM-based model to a new (i.e., target) data lake to minimize the annotations required on the new data lake. However, challenges include the source-target knowledge gap, selecting informative target data, and fine-tuning without losing shared knowledge exist. We propose LakeHopper, a framework that identifies and resolves the knowledge gap through LM interactions, employs a cluster-based data selection scheme for unannotated columns, and uses an incremental fine-tuning mechanism that gradually adapts the source model to the target data lake. Our experimental results validate the effectiveness of LakeHopper on two different data lake transfers under both low-resource and high-resource settings.

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