| --- |
| 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." |
| } |
| ``` |
