  Recent advances in tabular question answering (QA) with large language models
are constrained in their coverage and only answer questions over a single
table. However, real-world queries are complex in nature, often over multiple
tables in a relational database or web page. Single table questions do not
involve common table operations such as set operations, Cartesian products
(joins), or nested queries. Furthermore, multi-table operations often result in
a tabular output, which necessitates table generation capabilities of tabular
QA models. To fill this gap, we propose a new task of answering questions over
multiple tables. Our model, MultiTabQA, not only answers questions over
multiple tables, but also generalizes to generate tabular answers. To enable
effective training, we build a pre-training dataset comprising of 132,645 SQL
queries and tabular answers. Further, we evaluate the generated tables by
introducing table-specific metrics of varying strictness assessing various
levels of granularity of the table structure. MultiTabQA outperforms
state-of-the-art single table QA models adapted to a multi-table QA setting by
finetuning on three datasets: Spider, Atis and GeoQuery.
