---
license: apache-2.0
task_categories:
- question-answering
- multiple-choice
language:
- en
tags:
- mathematics
- algebra
- word-problems
- mcqa
- reasoning
size_categories:
- 10K<n<100K
---

# AQUA-RAT MCQA Dataset

This dataset contains the AQUA-RAT dataset converted to Multiple Choice Question Answering (MCQA) format with modifications.

## Dataset Description

AQUA-RAT is a dataset of algebraic word problems with rationales. This version has been processed to:
- Remove all questions where the correct answer was option "E" (5th choice)
- Remove the "E" option from all remaining questions (4 choices: A, B, C, D)
- Merge validation and test splits into a single test split

## Dataset Structure

Each example contains:
- `question`: The algebraic word problem
- `choices`: List of 4 possible answers (A, B, C, D)
- `answer_index`: Index of the correct answer (0-3)
- `answer_text`: Text of the correct answer
- `source`: Dataset source ("aqua_rat")
- `explanation`: Detailed rationale/solution

## Data Splits

- Train: 83671 examples
 - Test: 448 examples (merged validation + test)

## Usage

```python
from datasets import load_dataset

dataset = load_dataset("RikoteMaster/aqua-rat-mcqa")
```

## Original Dataset

This dataset is based on the AQUA-RAT dataset:
- Paper: https://arxiv.org/abs/1705.04146
- Original repository: https://huggingface.co/datasets/deepmind/aqua_rat

## Citation

```bibtex
@misc{ling2017program,
    title={Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems}, 
    author={Wang Ling and Dani Yogatama and Chris Dyer and Phil Blunsom},
    year={2017},
    eprint={1705.04146},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```