201 lines
6.9 KiB
Markdown
201 lines
6.9 KiB
Markdown
# C-MMLU
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## Overview
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C-MMLU (Chinese Massive Multitask Language Understanding) is a comprehensive Chinese evaluation benchmark covering 67 subjects across STEM, humanities, social sciences, and China-specific topics. It evaluates models' knowledge and reasoning in Chinese contexts.
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## Task Description
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- **Task Type**: Multiple-Choice Question Answering (Chinese)
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- **Input**: Chinese question with four answer choices (A, B, C, D)
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- **Output**: Single correct answer letter
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- **Subjects**: 67 subjects organized into categories including China-specific topics
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## Key Features
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- 67 subjects covering diverse Chinese knowledge domains
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- Includes China-specific topics (Chinese history, literature, civil service exam, etc.)
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- Questions from elementary to professional levels
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- Tests both general knowledge and China-specific cultural knowledge
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- Standard benchmark for Chinese language model evaluation
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## Evaluation Notes
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- Default configuration uses **0-shot** evaluation
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- Uses Chinese Chain-of-Thought (CoT) prompting template
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- Results can be aggregated by subject or category
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- Categories: STEM, Humanities, Social Science, China-specific, Other
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- Evaluates on test split
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## Properties
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| Property | Value |
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|----------|-------|
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| **Benchmark Name** | `cmmlu` |
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| **Dataset ID** | [evalscope/cmmlu](https://modelscope.cn/datasets/evalscope/cmmlu/summary) |
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| **Paper** | N/A |
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| **Tags** | `Chinese`, `Knowledge`, `MCQ` |
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| **Metrics** | `acc` |
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| **Default Shots** | 0-shot |
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| **Evaluation Split** | `test` |
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## Data Statistics
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| Metric | Value |
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|--------|-------|
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| Total Samples | 11,582 |
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| Prompt Length (Mean) | 197.87 chars |
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| Prompt Length (Min/Max) | 134 / 999 chars |
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**Per-Subset Statistics:**
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| Subset | Samples | Prompt Mean | Prompt Min | Prompt Max |
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|--------|---------|-------------|------------|------------|
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| `agronomy` | 169 | 168.76 | 142 | 266 |
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| `anatomy` | 148 | 157.72 | 141 | 224 |
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| `ancient_chinese` | 164 | 178.6 | 144 | 367 |
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| `arts` | 160 | 161.22 | 141 | 233 |
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| `astronomy` | 165 | 191.31 | 143 | 404 |
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| `business_ethics` | 209 | 175.15 | 146 | 291 |
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| `chinese_civil_service_exam` | 160 | 284.88 | 143 | 554 |
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| `chinese_driving_rule` | 131 | 181.57 | 151 | 250 |
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| `chinese_food_culture` | 136 | 170.49 | 139 | 270 |
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| `chinese_foreign_policy` | 107 | 254.16 | 150 | 381 |
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| `chinese_history` | 323 | 250.97 | 164 | 387 |
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| `chinese_literature` | 204 | 177.55 | 145 | 397 |
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| `chinese_teacher_qualification` | 179 | 207.4 | 156 | 326 |
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| `college_actuarial_science` | 106 | 270.99 | 163 | 558 |
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| `college_education` | 107 | 198.09 | 149 | 355 |
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| `college_engineering_hydrology` | 106 | 189.62 | 146 | 273 |
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| `college_law` | 108 | 220.14 | 157 | 310 |
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| `college_mathematics` | 105 | 343.1 | 174 | 999 |
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| `college_medical_statistics` | 106 | 212.85 | 151 | 450 |
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| `clinical_knowledge` | 237 | 245.74 | 150 | 393 |
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| `college_medicine` | 273 | 187.64 | 141 | 416 |
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| `computer_science` | 204 | 187.76 | 143 | 516 |
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| `computer_security` | 171 | 214.01 | 149 | 399 |
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| `conceptual_physics` | 147 | 222.4 | 154 | 337 |
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| `construction_project_management` | 139 | 186.58 | 149 | 306 |
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| `economics` | 159 | 184.19 | 149 | 259 |
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| `education` | 163 | 169.17 | 145 | 225 |
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| `elementary_chinese` | 252 | 174.84 | 142 | 368 |
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| `elementary_commonsense` | 198 | 163.93 | 139 | 247 |
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| `elementary_information_and_technology` | 238 | 181.63 | 143 | 275 |
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| `electrical_engineering` | 172 | 183.77 | 148 | 358 |
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| `elementary_mathematics` | 230 | 184.92 | 145 | 320 |
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| `ethnology` | 135 | 176.41 | 145 | 294 |
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| `food_science` | 143 | 165.87 | 141 | 240 |
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| `genetics` | 176 | 187.56 | 146 | 283 |
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| `global_facts` | 149 | 182.32 | 146 | 329 |
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| `high_school_biology` | 169 | 267.46 | 177 | 486 |
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| `high_school_chemistry` | 132 | 260.74 | 160 | 395 |
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| `high_school_geography` | 118 | 207.08 | 142 | 377 |
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| `high_school_mathematics` | 164 | 203.72 | 151 | 356 |
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| `high_school_physics` | 110 | 223.11 | 152 | 353 |
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| `high_school_politics` | 143 | 269.18 | 174 | 386 |
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| `human_sexuality` | 126 | 175.63 | 139 | 261 |
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| `international_law` | 185 | 199.09 | 150 | 385 |
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| `journalism` | 172 | 172.25 | 142 | 234 |
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| `jurisprudence` | 411 | 226.57 | 146 | 514 |
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| `legal_and_moral_basis` | 214 | 205.67 | 154 | 317 |
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| `logical` | 123 | 181.72 | 143 | 427 |
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| `machine_learning` | 122 | 213.32 | 155 | 419 |
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| `management` | 210 | 180.32 | 145 | 287 |
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| `marketing` | 180 | 185.59 | 144 | 247 |
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| `marxist_theory` | 189 | 190.72 | 145 | 273 |
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| `modern_chinese` | 116 | 207.66 | 142 | 471 |
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| `nutrition` | 145 | 173.48 | 144 | 267 |
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| `philosophy` | 105 | 179.91 | 143 | 359 |
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| `professional_accounting` | 175 | 183.38 | 147 | 281 |
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| `professional_law` | 211 | 231.1 | 150 | 414 |
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| `professional_medicine` | 376 | 174.87 | 144 | 319 |
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| `professional_psychology` | 232 | 173.55 | 142 | 273 |
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| `public_relations` | 174 | 178.06 | 144 | 263 |
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| `security_study` | 135 | 186.07 | 145 | 302 |
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| `sociology` | 226 | 173.89 | 145 | 384 |
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| `sports_science` | 165 | 170.49 | 141 | 283 |
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| `traditional_chinese_medicine` | 185 | 165.38 | 134 | 240 |
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| `virology` | 169 | 176.32 | 144 | 266 |
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| `world_history` | 161 | 258.64 | 167 | 388 |
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| `world_religions` | 160 | 163.08 | 142 | 235 |
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## Sample Example
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**Subset**: `agronomy`
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```json
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{
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"input": [
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{
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"id": "4e04de48",
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"content": "回答下面的单项选择题，请选出其中的正确答案。你的回答的最后一行应该是这样的格式：\"答案：[LETTER]\"（不带引号），其中 [LETTER] 是 A,B,C,D 中的一个。请在回答前进行一步步思考。\n\n问题：在农业生产中被当作极其重要的劳动对象发挥作用，最主要的不可替代的基本生产资料是\n选项：\nA) 农业生产工具\nB) 土地\nC) 劳动力\nD) 资金\n"
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}
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],
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"choices": [
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"农业生产工具",
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"土地",
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"劳动力",
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"资金"
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],
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"target": "B",
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"id": 0,
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"group_id": 0,
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"subset_key": "agronomy",
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"metadata": {
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"subject": "agronomy"
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}
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}
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```
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## Prompt Template
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**Prompt Template:**
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```text
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回答下面的单项选择题，请选出其中的正确答案。你的回答的最后一行应该是这样的格式："答案：[LETTER]"（不带引号），其中 [LETTER] 是 {letters} 中的一个。请在回答前进行一步步思考。
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问题：{question}
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选项：
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{choices}
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```
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## Usage
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### Using 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 cmmlu \
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--limit 10 # Remove this line for formal evaluation
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```
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### Using 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=['cmmlu'],
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dataset_args={
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'cmmlu': {
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# subset_list: ['agronomy', 'anatomy', 'ancient_chinese'] # optional, evaluate specific subsets
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}
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},
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limit=10, # Remove this line for formal evaluation
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)
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run_task(task_cfg=task_cfg)
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```
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