---
license: mit
dataset_info:
  features:
  - name: question_id
    dtype: string
  - name: question
    dtype: string
  - name: tables
    dtype: sequence
  - name: topic
    dtype: string
  - name: python_solution
    dtype: string
  - name: ground_truth
    dtype: string
   
---
## Dataset Description

**KnowledgeMath** is a knowledge-intensive dataset focused on mathematical reasoning within the domain of finance. It requires the model to comprehend specialized financial terminology and to interpret tabular data presented in the questions. 
**KnowledgeMath** includes **1200 QA examples** across 7 key areas in finance. These examples were collected from financial experts and feature detailed solution annotations in Python format.

## Dataset Information

- Paper: https://arxiv.org/abs/2311.09797
- Code: https://github.com/yale-nlp/KnowledgeMath
- Leaderboard: will be released soon!

### Data Downloading and Usage

All the data examples were divided into two subsets: *validation* and *test*.

- **validation**: 200 examples used for model development, validation, or for those with limited computing resources.
- **test**: 1000 examples for standard evaluation. We will not publicly release the annotated solution and answer for the test set.

You can download this dataset by the following command:

```python
from datasets import load_dataset

dataset = load_dataset("yale-nlp/KnowledgeMath")
```

Here are some examples of how to access the downloaded dataset:

```python
# print the first example on the validation set
print(dataset["validation"][0])

# print the first example on the test set
print(dataset["test"][0])
```

### Data Format

The dataset is provided in json format and contains the following attributes:

```json
{
    "question_id": [string] The question id,
    "question": [string] The question text,
    "tables": [list] List of Markdown-format tables associated with the question, 
    "python_solution": [string] Python-format and executable solution by financial experts. The code is written in a clear and executable format, with well-named variables and a detailed explanation,
    "ground_truth": [integer] Executed result of `python solution`, rounded to three decimal places,
    "topic": [string] The related financial area of the question
}
```

### Automated Evaluation

To automatically evaluate a model on **KnowledgeMath**, please refer to our GitHub repository [here](https://github.com/yale-nlp/KnowledgeMath).

## Citation

If you use the **KnowledgeMath** dataset in your work, please kindly cite the paper:

```
@misc{zhao2023knowledgemath,
      title={KnowledgeMath: Knowledge-Intensive Math Word Problem Solving in Finance Domains}, 
      author={Yilun Zhao and Hongjun Liu and Yitao Long and Rui Zhang and Chen Zhao and Arman Cohan},
      year={2023},
      eprint={2311.09797},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```