| --- |
| 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} |
| } |
| ``` |
|
|
