  Tabular data is ubiquitous in real-world applications and abundant on the
web, yet its annotation has traditionally required human labor, posing a
significant scalability bottleneck for tabular machine learning. Our
methodology can successfully annotate a large amount of tabular data and can be
flexibly steered to generate various types of annotations based on specific
research objectives, as we demonstrate with SQL annotation and input-target
column annotation as examples. As a result, we release AnnotatedTables, a
collection of 32,119 databases with LLM-generated annotations. The dataset
includes 405,616 valid SQL programs, making it the largest SQL dataset with
associated tabular data that supports query execution. To further demonstrate
the value of our methodology and dataset, we perform two follow-up research
studies. 1) We investigate whether LLMs can translate SQL programs to Rel
programs, a database language previously unknown to LLMs, while obtaining the
same execution results. Using our Incremental Prompt Engineering methods based
on execution feedback, we show that LLMs can produce adequate translations with
few-shot learning. 2) We evaluate the performance of TabPFN, a recent neural
tabular classifier trained on Bayesian priors, on 2,720 tables with
input-target columns identified and annotated by LLMs. On average, TabPFN
performs on par with the baseline AutoML method, though the relative
performance can vary significantly from one data table to another, making both
models viable for practical applications depending on the situation. Our
findings underscore the potential of LLMs in automating the annotation of large
volumes of diverse tabular data.
