Research in quantitative reasoning within the financial domain indeed
necessitates the use of realistic tasks and data, primarily because of the
significant impact of decisions made in business and finance. Financial
professionals often interact with documents hundreds of pages long, but most
research datasets drastically reduce this context length. To address this, we
introduce a long-document financial QA task. We augment 7,621 questions from
the existing FinQA dataset with full-document context, extending the average
context length for each question from under 700 words in FinQA to 123k words in
DocFinQA. We conduct extensive experiments of retrieval-based QA pipelines and
long-context language models on the augmented data. Our results show that
DocFinQA provides challenges for even the strongest, state-of-the-art systems.

在金融领域的定量推理研究中，由于商业和金融决策的重大影响，确实需要使用现实任务和数据。我们引入了一项长文档金融问答任务，通过在现有的 FinQA 数据集中添加完整文档的背景，将每个问题的平均上下文长度从 FinQA 的 700 个单词扩展到 DocFinQA 的 123k 个单词。我们对增强数据进行了广泛的基于检索的问答流程和长上下文语言模型的实验。结果表明，对于最强大的最先进系统来说，DocFinQA 提供了挑战。