---
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}
}
```