126 lines
3.5 KiB
Markdown
126 lines
3.5 KiB
Markdown
# GPQA-Diamond
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## 概述
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GPQA（Graduate-Level Google-Proof Q&A）Diamond 是一个极具挑战性的基准测试，包含 198 道由生物学、物理学和化学领域的专家编写的多项选择题。这些问题设计得极为困难，需要博士级别的专业知识才能正确作答。
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## 任务描述
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- **任务类型**：专家级多项选择问答
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- **输入**：研究生水平的科学问题，附带 4 个选项
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- **输出**：单个正确答案字母（A、B、C 或 D）
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- **领域**：生物学、物理学、化学
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## 主要特点
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- 198 道题目均由相关领域的博士专家编写并验证
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- 题目“无法通过 Google 轻易查到”——难以通过简单搜索获得答案
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- 旨在测试深层次的领域知识与推理能力
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- Diamond 子集代表了最高质量的问题
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- 人类专家平均准确率约为 65%，非专家约为 34%
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## 评估说明
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- 默认配置使用 **0-shot** 或 **5-shot** 评估
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- 支持思维链（Chain-of-Thought, CoT）提示以提升推理能力
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- 评估过程中答案选项会随机打乱
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- 仅使用训练集（验证集为私有）
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- 是衡量专家级推理能力的高难度基准测试
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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** | `gpqa_diamond` |
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| **数据集 ID** | [AI-ModelScope/gpqa_diamond](https://modelscope.cn/datasets/AI-ModelScope/gpqa_diamond/summary) |
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| **论文** | N/A |
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| **标签** | `Knowledge`, `MCQ` |
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| **指标** | `acc` |
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| **默认示例数（Shots）** | 0-shot |
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| **评估划分** | `train` |
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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 | 198 |
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| 提示词长度（平均） | 841.15 字符 |
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| 提示词长度（最小/最大） | 340 / 5845 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": "82b448a9",
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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. Think step by step before answering.\n\nTwo quantum states wi ... [TRUNCATED] ... and 10^-8 sec, respectively. We want to clearly distinguish these two energy levels. Which one of the following options could be their energy difference so that they can be clearly resolved?\n\n\nA) 10^-4 eV\nB) 10^-9 eV\nC) 10^-8 eV\nD) 10^-11 eV"
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}
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],
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"choices": [
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"10^-4 eV",
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"10^-9 eV",
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"10^-8 eV",
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"10^-11 eV"
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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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"subset_key": "",
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"metadata": {
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"correct_answer": "10^-4 eV",
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"incorrect_answers": [
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"10^-11 eV",
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"10^-8 eV\n",
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"10^-9 eV"
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]
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}
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}
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```
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*注：部分内容因展示需要已被截断。*
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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 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 gpqa_diamond \
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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=['gpqa_diamond'],
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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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``` |