kensho/FFDomainKnowledge

Finance Fundamentals: Domain Knowledge

This dataset contains 131 multiple choice questions designed to test a models domain knowledge in business and finance. For more information, see the BizBench paper.

Example

A fixed-rate system is characterized by:

A. explicit legislative commitment to maintain a specified parity.
B. monetary independence being subject to the maintenance of an exchange rate peg.
C. target foreign exchange reserves bearing a direct relationship to domestic monetary aggregates.

Citation

If you find this data useful, please cite:

@inproceedings{krumdick-etal-2024-bizbench,
    title = "{B}iz{B}ench: A Quantitative Reasoning Benchmark for Business and Finance",
    author = "Krumdick, Michael  and
      Koncel-Kedziorski, Rik  and
      Lai, Viet Dac  and
      Reddy, Varshini  and
      Lovering, Charles  and
      Tanner, Chris",
    editor = "Ku, Lun-Wei  and
      Martins, Andre  and
      Srikumar, Vivek",
    booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.acl-long.452/",
    doi = "10.18653/v1/2024.acl-long.452",
    pages = "8309--8332",
    abstract = "Answering questions within business and finance requires reasoning, precision, and a wide-breadth of technical knowledge. Together, these requirements make this domain difficult for large language models (LLMs). We introduce BizBench, a benchmark for evaluating models' ability to reason about realistic financial problems. BizBench comprises eight quantitative reasoning tasks, focusing on question-answering (QA) over financial data via program synthesis. We include three financially-themed code-generation tasks from newly collected and augmented QA data. Additionally, we isolate the reasoning capabilities required for financial QA: reading comprehension of financial text and tables for extracting intermediate values, and understanding financial concepts and formulas needed to calculate complex solutions. Collectively, these tasks evaluate a model{'}s financial background knowledge, ability to parse financial documents, and capacity to solve problems with code. We conduct an in-depth evaluation of open-source and commercial LLMs, comparing and contrasting the behavior of code-focused and language-focused models. We demonstrate that the current bottleneck in performance is due to LLMs' limited business and financial understanding, highlighting the value of a challenging benchmark for quantitative reasoning within this domain."
}