ZTab: Domain-Based Zero-Shot Annotation for Table Columns

This study addresses the challenge of automatically detecting semantic column types in relational tables, a key task in many real-world applications. Zero-shot modeling eliminates the need for user-provided labeled training data, making it ideal for scenarios where data collection is costly or restricted due to issues such as privacy concerns. However, existing zeroshot models suffer from poor performance in the case of a large number of semantic column types or classes, poor understanding of tabular structures, and privacy risks arising from dependency on high-performance closed-source LLMs. We introduce ZTab, a domain-based zero-shot framework, to address both performance and zero-shot requirements. ZTab considers a domain configuration given by a set of predefined semantic types, plus sample table schemas based on such types, fine-tunes an annotation LLM using pseudo-tables generated for sample table schemas. ZTab is domain-based zero-shot in that it does not depend on user-specific labeled training data; therefore, no retraining is needed for a test table coming from a similar domain. We describe three cases for domain-based zero-shot. The domain configuration of ZTab provides a trade-off between the extent of zero-shot and the annotation performance: for a "universal domain" that contains all semantic types, domainbased zero-shot will approach "pure" zero-shot; on the other hand, a "specialized domain" that contains semantic types for a specific application will enable better zero-shot performance within that domain. The source code and datasets are available at https://github.com/hoseinzadeehsan/ZTab.