README.md

metadata

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

{
    "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}
}