LakeHopper: Knowledge-Aware Adaptation of Column Type Annotators across Data Lakes

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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 one particular corpus of tables, i.e., a source data lake, and they degrade sharply once deployed on a new (i.e., target) lake, whose tables and semantic type set both differ. Retraining per lake is prohibitive because it demands large volumes of expert annotations. We recast cross-lake adaptation as a knowledge management problem and make the resulting decomposition explicit: relative to a target annotator, a source annotator holds knowledge that must be discarded (source-specific), realigned and reused (shared), or acquired (target-specific). This decomposition exposes which part of the gap a general-purpose LLM can close and which part only target supervision can. Guided by it, we present LakeHopper, which adapts a source annotator under a fixed annotation budget through three coupled mechanisms: label-set realignment that transplants the output layer for shared types, LLM-verified gap discovery that localizes columns the annotator handles unreliably, and cluster-based propagation plus rehearsal fine-tuning that generalizes each flagged column into a labeling batch without erasing shared knowledge. Casting the LLM as a verifier of the annotator's own predictions rather than an annotator keeps every output inside the target type set, so LakeHopper structurally emits no out-of-domain labels, …

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