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.

本文提出了一种新的多表问题回答模型，称作 MultiTabQA，除了回答多表问题外，还能生成表格回答。为了实现有效的训练，我们构建了一个包括 132,645 个 SQL 查询和表格回答的预训练数据集。通过引入不同严格程度的特定于表格的评估指标，我们评估了生成的表格。经过在三个数据集（Spider、Atis 和 GeoQuery）上微调后，MultiTabQA 优于在多表 QA 环境中改编成的最先进的单表 QA 模型。