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---
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:
- [TatQA](https://arxiv.org/abs/2105.07624)
- [ConvFinQA](https://arxiv.org/abs/2210.03849)
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.](https://aclanthology.org/2024.acl-long.452.pdf)
## 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."
}
``` |