metadata

dataset_info:
  features:
    - name: question
      dtype: string
    - name: answer
      dtype: float64
    - name: program
      dtype: string
    - name: task
      dtype: string
    - name: context
      dtype: string
  splits:
    - name: train
      num_bytes: 306143
      num_examples: 246
  download_size: 144247
  dataset_size: 306143
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

Finance Fundamentals: Program Synthesis

This dataset contains 246 quantative reasoning financial math word problems, using data sourced from:

Each question is annotated with both a numeric target solution, along with a python program containing the explicit steps to reach that solution. For more information, see the BizBench paper.

Example

The market price of K-T-Lew Corporation's common stock is $60 per share, and each share gives its owner one subscription right. Four rights are required to purchase an additional share of common stock at the subscription price of $54 per share. If the common stock is currently selling rights-on, what is the theoretical value of a right? Answer to the nearest cent.

stock_price = 60.0
rights_sub_price_per_share = 54.0
rights_per_share = 4
value = (stock_price - rights_sub_price_per_share) / (rights_per_share + 1)
round(value, 2)

1.2

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."
}