  Schema matching constitutes a pivotal phase in the data ingestion process for
contemporary database systems. Its objective is to discern pairwise
similarities between two sets of attributes, each associated with a distinct
data table. This challenge emerges at the initial stages of data analytics,
such as when incorporating a third-party table into existing databases to
inform business insights. Given its significance in the realm of database
systems, schema matching has been under investigation since the 2000s. This
study revisits this foundational problem within the context of large language
models. Adhering to increasingly stringent data security policies, our focus
lies on the zero-shot and few-shot scenarios: the model should analyze only a
minimal amount of customer data to execute the matching task, contrasting with
the conventional approach of scrutinizing the entire data table. We emphasize
that the zero-shot or few-shot assumption is imperative to safeguard the
identity and privacy of customer data, even at the potential cost of accuracy.
The capability to accurately match attributes under such stringent requirements
distinguishes our work from previous literature in this domain.
