6.7 KiB
6.7 KiB
MMLU
概述
MMLU（Massive Multitask Language Understanding，大规模多任务语言理解）是一个综合性评估基准，旨在衡量模型在预训练阶段所获得的知识。它涵盖 STEM、人文学科、社会科学及其他领域的 57 个学科，难度从基础到专业级别不等。
任务描述
- 任务类型：多项选择题问答（Multiple-Choice Question Answering）
- 输入：包含四个选项（A、B、C、D）的问题
- 输出：单个正确答案的字母
- 学科范围：57 个学科，分为 4 个类别（STEM、人文学科、社会科学、其他）
主要特点
- 覆盖从基础到高级专业水平的多样化知识领域
- 同时考察事实性知识和推理能力
- 包含抽象代数、解剖学、天文学、商业伦理等多个学科
- 是衡量大语言模型知识广度的标准基准
评估说明
- 默认配置使用开发集（dev split）中的 5-shot 示例
- 支持思维链（Chain-of-Thought, CoT）提示以提升推理能力
- 结果可按学科或类别（STEM、人文学科、社会科学、其他）进行聚合
- 使用
subset_list参数可评估特定学科
属性
| 属性 | 值 |
|---|---|
| 基准测试名称 | mmlu |
| 数据集 ID | cais/mmlu |
| 论文 | N/A |
| 标签 | Knowledge, MCQ |
| 指标 | acc |
| 默认示例数量 | 5-shot |
| 评估集 | test |
| 训练集 | dev |
数据统计
| 指标 | 值 |
|---|---|
| 总样本数 | 14,042 |
| 提示词长度（平均） | 3212.2 字符 |
| 提示词长度（最小/最大） | 985 / 14626 字符 |
各子集统计数据：
| 子集 | 样本数 | 提示平均长度 | 提示最小长度 | 提示最大长度 |
|---|---|---|---|---|
abstract_algebra |
100 | 1256.98 | 1143 | 1383 |
anatomy |
135 | 1446 | 1306 | 1783 |
astronomy |
152 | 2616.64 | 2401 | 3191 |
business_ethics |
100 | 2756.36 | 2492 | 3145 |
clinical_knowledge |
265 | 1680.76 | 1510 | 1995 |
college_biology |
144 | 2104.53 | 1874 | 2641 |
college_chemistry |
100 | 1800.19 | 1641 | 2239 |
college_computer_science |
100 | 3424.74 | 3129 | 4137 |
college_mathematics |
100 | 1972.77 | 1776 | 2230 |
college_medicine |
173 | 2377.62 | 2005 | 6779 |
college_physics |
102 | 1941.26 | 1757 | 2237 |
computer_security |
100 | 1598.89 | 1414 | 2238 |
conceptual_physics |
235 | 1340.77 | 1246 | 1572 |
econometrics |
114 | 2286.2 | 1976 | 2708 |
electrical_engineering |
145 | 1372.1 | 1279 | 1578 |
elementary_mathematics |
378 | 1856.81 | 1724 | 2322 |
formal_logic |
126 | 2338.07 | 2065 | 3022 |
global_facts |
100 | 1644.77 | 1558 | 2001 |
high_school_biology |
310 | 2218.56 | 1971 | 2737 |
high_school_chemistry |
203 | 1740.83 | 1519 | 2341 |
high_school_computer_science |
100 | 3595.34 | 3224 | 4732 |
high_school_european_history |
165 | 13421.12 | 12392 | 14626 |
high_school_geography |
198 | 1849.07 | 1726 | 2204 |
high_school_government_and_politics |
193 | 2355.29 | 2171 | 2922 |
high_school_macroeconomics |
390 | 1861.47 | 1649 | 2206 |
high_school_mathematics |
270 | 1731.31 | 1591 | 2224 |
high_school_microeconomics |
238 | 1849.97 | 1651 | 2396 |
high_school_physics |
151 | 2110.63 | 1836 | 2926 |
high_school_psychology |
545 | 2431.39 | 2223 | 3498 |
high_school_statistics |
216 | 3273.79 | 2932 | 4328 |
high_school_us_history |
204 | 10530.61 | 9656 | 11469 |
high_school_world_history |
237 | 6700.7 | 5650 | 8814 |
human_aging |
223 | 1448.65 | 1335 | 1725 |
human_sexuality |
131 | 1556.02 | 1412 | 2250 |
international_law |
121 | 3094.31 | 2778 | 3432 |
jurisprudence |
108 | 1851.24 | 1638 | 2370 |
logical_fallacies |
163 | 2114.39 | 1932 | 2503 |
machine_learning |
112 | 2852.01 | 2659 | 3150 |
management |
103 | 1326.07 | 1220 | 1571 |
marketing |
234 | 1984.28 | 1832 | 2266 |
medical_genetics |
100 | 1531.32 | 1409 | 1775 |
miscellaneous |
783 | 1121.57 | 1004 | 2096 |
moral_disputes |
346 | 2300.58 | 2101 | 2711 |
moral_scenarios |
895 | 2709.89 | 2644 | 2853 |
nutrition |
306 | 2620.9 | 2408 | 3111 |
philosophy |
311 | 1472.67 | 1319 | 2292 |
prehistory |
324 | 2388.29 | 2201 | 2899 |
professional_accounting |
282 | 2820.72 | 2515 | 3400 |
professional_law |
1,534 | 8077.0 | 6997 | 10539 |
professional_medicine |
272 | 4832.72 | 4380 | 5802 |
professional_psychology |
612 | 2860.73 | 2594 | 3789 |
public_relations |
110 | 1991.35 | 1824 | 2712 |
security_studies |
245 | 6405.04 | 5680 | 7818 |
sociology |
201 | 2176.49 | 1976 | 2530 |
us_foreign_policy |
100 | 2129.28 | 1944 | 2393 |
virology |
166 | 1563.11 | 1426 | 2507 |
world_religions |
171 | 1051.68 | 985 | 1255 |
样例示例
子集: abstract_algebra
{
"input": [
{
"id": "c7cbfbb9",
"content": "Here are some examples of how to answer similar questions:\n\nFind all c in Z_3 such that Z_3[x]/(x^2 + c) is a field.\nA) 0\nB) 1\nC) 2\nD) 3\nANSWER: B\n\nStatement 1 | If aH is an element of a factor group, then |aH| divides |a|. Statement 2 | If H ... [TRUNCATED] ... d 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\nFind the degree for the given field extension Q(sqrt(2), sqrt(3), sqrt(18)) over Q.\n\nA) 0\nB) 4\nC) 2\nD) 6"
}
],
"choices": [
"0",
"4",
"2",
"6"
],
"target": "B",
"id": 0,
"group_id": 0,
"subset_key": "abstract_algebra",
"metadata": {
"subject": "abstract_algebra"
}
}
注：部分内容为显示目的已截断。
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 mmlu \
--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=['mmlu'],
dataset_args={
'mmlu': {
# subset_list: ['abstract_algebra', 'anatomy', 'astronomy'] # 可选，用于评估特定子集
}
},
limit=10, # 正式评估时请删除此行
)
run_task(task_cfg=task_cfg)