Datasets:
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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}
}
``` |