MathQA#

概述#

MathQA 是一个大规模数学应用题求解数据集，通过对 AQuA-RAT 数据集进行标注而构建，使用了一种新的表示语言为每个问题提供了完整的可执行操作程序。该数据集包含多样化的数学问题，需要多步推理才能解答。

任务描述#

  • 任务类型：数学推理（多项选择题）

  • 输入：带有多个选项的数学应用题

  • 输出：正确答案及逐步推理过程（Chain-of-Thought）

  • 难度：多样（从小学到中级水平）

主要特点#

  • 标注了可执行的操作程序

  • 考察量化推理与问题解决能力

  • 涵盖多种数学主题和题型

  • 多项选择格式，附带结构化解法

  • 适用于评估模型的数学推理能力

评估说明#

  • 默认配置采用 0-shot 评估方式

  • 使用思维链（Chain-of-Thought, CoT）提示进行推理

  • 在测试集（test split）上进行评估

  • 使用简单准确率（accuracy）作为评估指标

  • 推理步骤可在元数据（metadata）中获取

属性#

属性

值

基准测试名称

math_qa

数据集ID

extraordinarylab/math-qa

论文

N/A

标签

MCQ, Math, Reasoning

指标

acc

默认示例数

0-shot

评估划分

test

数据统计#

指标

值

总样本数

2,985

提示词长度（平均）

433.02 字符

提示词长度（最小/最大）

257 / 879 字符

样例示例#

子集: default

{
  "input": [
    {
      "id": "e77fca06",
      "content": "Answer the following multiple choice question. The last line of your response should be of the following format: 'ANSWER: [LETTER]' (without quotes) where [LETTER] is one of A,B,C,D,E. Think step by step before answering.\n\na shopkeeper sold an article offering a discount of 5 % and earned a profit of 31.1 % . what would have been the percentage of profit earned if no discount had been offered ?\n\nA) 38\nB) 27.675\nC) 30\nD) data inadequate\nE) none of these"
    }
  ],
  "choices": [
    "38",
    "27.675",
    "30",
    "data inadequate",
    "none of these"
  ],
  "target": "A",
  "id": 0,
  "group_id": 0,
  "metadata": {
    "reasoning": "\"giving no discount to customer implies selling the product on printed price . suppose the cost price of the article is 100 . then printed price = 100 ã — ( 100 + 31.1 ) / ( 100 â ˆ ’ 5 ) = 138 hence , required % profit = 138 â € “ 100 = 38 % answer a\""
  }
}

提示模板#

提示模板：

Answer the following multiple choice question. The last line of your response should be of the following format: 'ANSWER: [LETTER]' (without quotes) where [LETTER] is one of {letters}. Think step by step before answering.

{question}

{choices}

使用方法#

使用命令行（CLI）#

evalscope eval \
    --model YOUR_MODEL \
    --api-url OPENAI_API_COMPAT_URL \
    --api-key EMPTY_TOKEN \
    --datasets math_qa \
    --limit 10  # 正式评估时请删除此行

使用 Python#

from evalscope import run_task
from evalscope.config import TaskConfig

task_cfg = TaskConfig(
    model='YOUR_MODEL',
    api_url='OPENAI_API_COMPAT_URL',
    api_key='EMPTY_TOKEN',
    datasets=['math_qa'],
    limit=10,  # 正式评估时请删除此行
)

run_task(task_cfg=task_cfg)