  Data management has traditionally relied on synthetic data generators to
generate structured benchmarks, like the TPC suite, where we can control
important parameters like data size and its distribution precisely. These
benchmarks were central to the success and adoption of database management
systems. But more and more, data management problems are of a semantic nature.
An important example is finding tables that can be unioned. While any two
tables with the same cardinality can be unioned, table union search is the
problem of finding tables whose union is semantically coherent. Semantic
problems cannot be benchmarked using synthetic data. Our current methods for
creating benchmarks involve the manual curation and labeling of real data.
These methods are not robust or scalable and perhaps more importantly, it is
not clear how robust the created benchmarks are. We propose to use generative
AI models to create structured data benchmarks for table union search. We
present a novel method for using generative models to create tables with
specified properties. Using this method, we create a new benchmark containing
pairs of tables that are both unionable and non-unionable but related. We
thoroughly evaluate recent existing table union search methods over existing
benchmarks and our new benchmark. We also present and evaluate a new table
search methods based on recent large language models over all benchmarks. We
show that the new benchmark is more challenging for all methods than
hand-curated benchmarks, specifically, the top-performing method achieves a
Mean Average Precision of around 60%, over 30% less than its performance on
existing manually created benchmarks. We examine why this is the case and show
that the new benchmark permits more detailed analysis of methods, including a
study of both false positives and false negatives that were not possible with
existing benchmarks.
