| --- |
| license: cc-by-4.0 |
| task_categories: |
| - text-generation |
| language: |
| - zh |
| size_categories: |
| - n<1K |
| tags: |
| - chinese |
| - math-word-problems |
| - multiple-choice-qa |
| - mathematical-reasoning |
| modalities: |
| - text |
| library_name: datasets |
| --- |
| |
| ## Introduction |
| MathMC is a Chinese mathematical reasoning dataset introduced in the paper **Teaching-Inspired Integrated Prompting Framework: A Novel Approach for Enhancing Reasoning in Large Language Models.** |
| It contains **1,000 Chinese multiple-choice math problems**, each annotated with a **gold answer** and a **detailed rationale**. |
|
|
| ## Dataset Structure |
|
|
| ### Data Fields |
| - `qtype`: question type. In MathMC, this is typically `"CHOICE"` for multiple-choice questions. |
| - `quest_stem`: the main question content. |
| - `quest_stem.text`: the problem statement in Chinese. |
| - `quest_stem.options`: a list of answer options. |
| - `bullet`: the option label, such as `"A"`, `"B"`, `"C"`. |
| - `text`: the content of the option. |
| - `quest_ref`: reference answers and explanations. |
| - `quest_ref.texts`: the correct answer(s), stored as a list. |
| - `quest_ref.analyses`: the explanation(s) or rationale(s), stored as a list. |
|
|
| ### Example |
|
|
| ```json |
| { |
| "qtype": "CHOICE", |
| "quest_stem": { |
| "options": [ |
| { |
| "bullet": "A", |
| "text": "扩大到原来的10倍" |
| }, |
| { |
| "bullet": "B", |
| "text": "扩大到原来的100倍" |
| }, |
| { |
| "bullet": "C", |
| "text": "扩大到原来的1000倍" |
| } |
| ], |
| "text": "在计算7.2÷0.12时，需要把被除数和除数同时( ) " |
| }, |
| "quest_ref": { |
| "texts": [ |
| "B" |
| ], |
| "analyses": [ |
| "根据商不变性质:被除数和除数同时扩大或缩小相同的倍数(0除外)，商不变;据此解答." |
| ] |
| } |
| } |
| ``` |
|
|
| ### Dataset Statistics |
|
|
| According to the paper, the question-type distribution of MathMC is: |
|
|
| - Arithmetic: 619 |
| - Algebra: 113 |
| - Geometry: 227 |
| - Statistics: 27 |
| - Reasoning: 7 |
| - Others: 7 |
|
|
| Total: 1,000 questions. |
|
|
| ## Citation |
|
|
| If you use this dataset, please cite the following paper: |
| ``` |
| @inproceedings{tan-etal-2025-teaching, |
| title = {Teaching-Inspired Integrated Prompting Framework: A Novel Approach for Enhancing Reasoning in Large Language Models}, |
| author = {Tan, Wenting and Chen, Dongxiao and Xue, Jieting and Wang, Zihao and Chen, Taijie}, |
| booktitle = {Proceedings of the 31st International Conference on Computational Linguistics: Industry Track}, |
| pages = {827--839}, |
| year = {2025}, |
| publisher = {Association for Computational Linguistics} |
| } |
| ``` |
