LakeHopper: Cross Data Lakes Column Type Annotation through Model Adaptation
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.
Cite
@article{arxiv.2602.08793,
title = {LakeHopper: Cross Data Lakes Column Type Annotation through Model Adaptation},
author = {Yushi Sun and Xujia Li and Nan Tang and Quanqing Xu and Chuanhui Yang and Lei Chen},
journal= {arXiv preprint arXiv:2602.08793},
year = {2026}
}