| --- |
| dataset_info: |
| features: |
| - name: problem |
| dtype: string |
| - name: solution |
| dtype: string |
| - name: answer |
| dtype: string |
| - name: subject |
| dtype: string |
| - name: level |
| dtype: int64 |
| - name: unique_id |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 9803889 |
| num_examples: 12000 |
| - name: test |
| num_bytes: 400274 |
| num_examples: 500 |
| download_size: 5333852 |
| dataset_size: 10204163 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| - split: test |
| path: data/test-* |
| --- |
| |
| # Hendrycks MATH Dataset |
|
|
| ## Dataset Description |
|
|
| The MATH dataset is a collection of mathematics competition problems designed to evaluate mathematical reasoning and problem-solving capabilities in computational systems. Containing 12,500 high school competition-level mathematics problems, this dataset is notable for including detailed step-by-step solutions alongside each problem. |
|
|
| ### Dataset Summary |
|
|
| The dataset consists of mathematics problems spanning multiple difficulty levels (1-5) and various mathematical subjects including: |
|
|
| - Prealgebra |
| - Algebra |
| - Number Theory |
| - Counting and Probability |
| - Geometry |
| - Intermediate Algebra |
| - Precalculus |
|
|
| Each problem comes with: |
| - A complete problem statement |
| - A step-by-step solution |
| - A final answer |
| - Difficulty rating |
| - Subject classification |
|
|
| ### Data Split |
|
|
| The dataset is divided into: |
| - Training set: 12,000 |
| - Test set: 500 problems |
|
|
| ## Dataset Creation |
|
|
| ### Citation |
|
|
| ``` |
| @article{hendrycksmath2021, |
| title={Measuring Mathematical Problem Solving With the MATH Dataset}, |
| author={Dan Hendrycks |
| and Collin Burns |
| and Saurav Kadavath |
| and Akul Arora |
| and Steven Basart |
| and Eric Tang |
| and Dawn Song |
| and Jacob Steinhardt}, |
| journal={arXiv preprint arXiv:2103.03874}, |
| year={2021} |
| } |
| ``` |
|
|
| ### Source Data |
|
|
| The problems originate from high school mathematics competitions, including competitions like the AMC 10, AMC 12, and AIME. These represent carefully curated, high-quality mathematical problems that test conceptual understanding and problem-solving abilities rather than just computational skills. |
|
|
| ### Annotations |
|
|
| Each problem includes: |
| - Complete problem text in LaTeX format |
| - Detailed solution steps |
| - Final answer in a standardized format |
| - Subject category |
| - Difficulty level (1-5) |
|
|
| ### Papers and References |
|
|
| For detailed information about the dataset and its evaluation, refer to "Measuring Mathematical Problem Solving With the MATH Dataset" presented at NeurIPS 2021. |
|
|
| https://arxiv.org/pdf/2103.03874 |
