MultiTabQA: Generating Tabular Answers for Multi-Table Question Answering
Vaishali Pal, Andrew Yates, Evangelos Kanoulas, Maarten de Rijke · 2023
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.