metadata
license: apache-2.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: subject
dtype: string
- name: question
dtype: string
- name: A
dtype: string
- name: B
dtype: string
- name: C
dtype: string
- name: D
dtype: string
- name: answer
dtype: string
- name: task
dtype: string
splits:
- name: train
num_bytes: 52881
num_examples: 131
download_size: 36977
dataset_size: 52881
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."
}