| --- |
| license: cc-by-4.0 |
| task_categories: |
| - text-generation |
| language: |
| - zh |
| size_categories: |
| - n<1K |
| tags: |
| - chinese |
| - math-word-problems |
| - true-false |
| - mathematical-reasoning |
| modalities: |
| - text |
| libraries: |
| - Datasets |
| --- |
| |
| ## Introduction |
| MathToF 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 true-or-false math problems**, each annotated with a **binary label (True/False)** and a **detailed rationale**. |
|
|
| ## Dataset Structure |
|
|
| ### Data Fields |
| - `qtype`: question type. In MathToF, this is typically `"JUDGE"`. |
| - `quest_stem`: the main question content. |
| - `quest_stem.text`: the problem statement in Chinese. |
| - `quest_ref`: reference answers and explanations. |
| - `quest_ref.texts`: the ground-truth label(s), typically `"True"` or `"False"`. |
| - `quest_ref.analyses`: the explanation(s), stored as a list. |
|
|
| ### Example |
|
|
| ```json |
| { |
| "qtype": "JUDGE", |
| "quest_stem": { |
| "text": "三位数减三位数差一定是三位数。" |
| }, |
| "quest_ref": { |
| "texts": [ |
| "False" |
| ], |
| "analyses": [ |
| "如两个三位数分别为:150，100。则150-100=50，其差50是两位数，故题干描述错误。" |
| ] |
| } |
| } |
| ``` |
|
|
| ### Dataset Statistics |
|
|
| According to the paper, the question-type distribution of MathToF is: |
|
|
| - Arithmetic: 675 |
| - Algebra: 61 |
| - Geometry: 197 |
| - Statistics: 37 |
| - Reasoning: 13 |
| - Others: 17 |
|
|
| 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} |
| } |
| ``` |
