Several large-scale datasets (e.g., WikiSQL, Spider) for developing natural
language interfaces to databases have recently been proposed. These datasets
cover a wide breadth of domains but fall short on some essential domains, such
as finance and accounting. Given that accounting databases are used worldwide,
particularly by non-technical people, there is an imminent need to develop
models that could help extract information from accounting databases via
natural language queries. In this resource paper, we aim to fill this gap by
proposing a new large-scale Text-to-SQL dataset for the accounting and
financial domain: BookSQL. The dataset consists of 100k natural language
queries-SQL pairs, and accounting databases of 1 million records. We experiment
with and analyze existing state-of-the-art models (including GPT-4) for the
Text-to-SQL task on BookSQL. We find significant performance gaps, thus
pointing towards developing more focused models for this domain.

给定一份新的大规模文本转 SQL 数据集（BookSQL），研究作者通过实验与分析现有的最先进模型，发现在会计和金融领域存在显著的性能差距，进而指出有需要开发更专注于该领域的模型。