# GPQA-Diamond ## 概述 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。 ## 任务描述 - **任务类型**:专家级多项选择问答 - **输入**:研究生水平的科学问题,附带 4 个选项 - **输出**:单个正确答案字母(A、B、C 或 D) - **领域**:生物学、物理学、化学 ## 主要特点 - 198 道题目均由相关领域的博士专家编写并验证 - 题目“无法通过 Google 轻易查到”——难以通过简单搜索获得答案 - 旨在测试深层次的领域知识与推理能力 - Diamond 子集代表了最高质量的问题 - 人类专家平均准确率约为 65%,非专家约为 34% ## 评估说明 - 默认配置使用 **0-shot** 或 **5-shot** 评估 - 支持思维链(Chain-of-Thought, CoT)提示以提升推理能力 - 评估过程中答案选项会随机打乱 - 仅使用训练集(验证集为私有) - 是衡量专家级推理能力的高难度基准测试 ## 属性 | 属性 | 值 | |----------|-------| | **基准测试名称** | `gpqa_diamond` | | **数据集 ID** | [AI-ModelScope/gpqa_diamond](https://modelscope.cn/datasets/AI-ModelScope/gpqa_diamond/summary) | | **论文** | N/A | | **标签** | `Knowledge`, `MCQ` | | **指标** | `acc` | | **默认示例数(Shots)** | 0-shot | | **评估划分** | `train` | ## 数据统计 | 指标 | 值 | |--------|-------| | 总样本数 | 198 | | 提示词长度(平均) | 841.15 字符 | | 提示词长度(最小/最大) | 340 / 5845 字符 | ## 样例示例 **子集**: `default` ```json { "input": [ { "id": "82b448a9", "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" } ], "choices": [ "10^-4 eV", "10^-9 eV", "10^-8 eV", "10^-11 eV" ], "target": "A", "id": 0, "group_id": 0, "subset_key": "", "metadata": { "correct_answer": "10^-4 eV", "incorrect_answers": [ "10^-11 eV", "10^-8 eV\n", "10^-9 eV" ] } } ``` *注:部分内容因展示需要已被截断。* ## 提示模板 **提示模板:** ```text 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 CLI ```bash evalscope eval \ --model YOUR_MODEL \ --api-url OPENAI_API_COMPAT_URL \ --api-key EMPTY_TOKEN \ --datasets gpqa_diamond \ --limit 10 # 正式评估时请删除此行 ``` ### 使用 Python ```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=['gpqa_diamond'], limit=10, # 正式评估时请删除此行 ) run_task(task_cfg=task_cfg) ```