117 lines
3.4 KiB
Markdown
117 lines
3.4 KiB
Markdown
# MathQA
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## 概述
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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。
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## 任务描述
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- **任务类型**：数学推理（多项选择题）
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- **输入**：带有多个选项的数学应用题
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- **输出**：正确答案及逐步推理过程（Chain-of-Thought）
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- **难度**：多样（从小学到中级水平）
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## 主要特点
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- 标注了可执行的操作程序
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- 考察量化推理与问题解决能力
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- 涵盖多种数学主题和题型
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- 多项选择格式，附带结构化解法
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- 适用于评估模型的数学推理能力
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## 评估说明
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- 默认配置采用 **0-shot** 评估方式
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- 使用思维链（Chain-of-Thought, CoT）提示进行推理
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- 在测试集（test split）上进行评估
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- 使用简单准确率（accuracy）作为评估指标
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- 推理步骤可在元数据（metadata）中获取
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## 属性
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| 属性 | 值 |
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|----------|-------|
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| **基准测试名称** | `math_qa` |
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| **数据集ID** | [extraordinarylab/math-qa](https://modelscope.cn/datasets/extraordinarylab/math-qa/summary) |
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| **论文** | N/A |
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| **标签** | `MCQ`, `Math`, `Reasoning` |
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| **指标** | `acc` |
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| **默认示例数** | 0-shot |
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| **评估划分** | `test` |
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## 数据统计
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| 指标 | 值 |
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|--------|-------|
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| 总样本数 | 2,985 |
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| 提示词长度（平均） | 433.02 字符 |
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| 提示词长度（最小/最大） | 257 / 879 字符 |
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## 样例示例
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**子集**: `default`
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```json
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{
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"input": [
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{
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"id": "e77fca06",
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"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"
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}
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],
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"choices": [
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"38",
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"27.675",
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"30",
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"data inadequate",
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"none of these"
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],
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"target": "A",
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"id": 0,
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"group_id": 0,
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"metadata": {
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"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\""
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}
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}
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```
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## 提示模板
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**提示模板：**
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```text
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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.
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{question}
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{choices}
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```
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## 使用方法
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### 使用命令行（CLI）
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```bash
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evalscope eval \
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--model YOUR_MODEL \
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--api-url OPENAI_API_COMPAT_URL \
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--api-key EMPTY_TOKEN \
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--datasets math_qa \
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--limit 10 # 正式评估时请删除此行
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```
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### 使用 Python
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```python
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from evalscope import run_task
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from evalscope.config import TaskConfig
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task_cfg = TaskConfig(
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model='YOUR_MODEL',
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api_url='OPENAI_API_COMPAT_URL',
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api_key='EMPTY_TOKEN',
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datasets=['math_qa'],
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limit=10, # 正式评估时请删除此行
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)
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run_task(task_cfg=task_cfg)
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``` |