147 lines
4.3 KiB
Markdown
147 lines
4.3 KiB
Markdown
# MMMLU
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## Overview
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MMMLU (Multilingual Massive Multitask Language Understanding) is a multilingual extension of the MMLU benchmark. It evaluates the multilingual knowledge and reasoning capabilities of language models across 14 languages, covering 57 subjects from the original MMLU benchmark.
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## Task Description
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- **Task Type**: Multilingual Multiple-Choice Question Answering
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- **Input**: Question with four answer choices (A, B, C, D) in one of 14 languages
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- **Output**: Single correct answer letter
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- **Languages**: Arabic, Bengali, German, Spanish, French, Hindi, Indonesian, Italian, Japanese, Korean, Portuguese, Swahili, Yoruba, Chinese
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- **Subjects**: 57 subjects from MMLU (STEM, Humanities, Social Sciences, Other)
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## Key Features
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- Multilingual translation of the full MMLU benchmark
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- 14 typologically diverse languages covering major language families
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- Tests cross-lingual knowledge transfer and multilingual reasoning
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- Same subject coverage as original MMLU (57 subjects)
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- Includes low-resource languages (e.g., Swahili, Yoruba)
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## Evaluation Notes
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- Default configuration uses **0-shot** evaluation (test split only)
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- Use `subset_list` to evaluate specific languages (e.g., `['ZH_CN', 'JA_JP', 'FR_FR']`)
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- Results are grouped by language subset
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- Cross-lingual performance comparison supported
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## Properties
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| Property | Value |
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|----------|-------|
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| **Benchmark Name** | `mmmlu` |
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| **Dataset ID** | [openai-mirror/MMMLU](https://modelscope.cn/datasets/openai-mirror/MMMLU/summary) |
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| **Paper** | N/A |
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| **Tags** | `Knowledge`, `MCQ`, `MultiLingual` |
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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 | 196,588 |
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| Prompt Length (Mean) | 624.75 chars |
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| Prompt Length (Min/Max) | 136 / 5975 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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| `AR_XY` | 14,042 | 584.94 | 231 | 4735 |
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| `BN_BD` | 14,042 | 654.99 | 247 | 4914 |
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| `DE_DE` | 14,042 | 791.64 | 294 | 5657 |
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| `ES_LA` | 14,042 | 753.18 | 271 | 5791 |
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| `FR_FR` | 14,042 | 777.82 | 278 | 5952 |
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| `HI_IN` | 14,042 | 675.02 | 256 | 5379 |
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| `ID_ID` | 14,042 | 726.51 | 270 | 5539 |
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| `IT_IT` | 14,042 | 761.19 | 277 | 5975 |
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| `JA_JP` | 14,042 | 322.79 | 149 | 2064 |
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| `KO_KR` | 14,042 | 354.35 | 153 | 2345 |
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| `PT_BR` | 14,042 | 706.79 | 258 | 5635 |
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| `SW_KE` | 14,042 | 699.08 | 259 | 5566 |
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| `YO_NG` | 14,042 | 681.01 | 248 | 5644 |
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| `ZH_CN` | 14,042 | 257.15 | 136 | 1495 |
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## Sample Example
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**Subset**: `AR_XY`
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```json
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{
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"input": [
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{
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"id": "e43faf14",
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"content": "أجب على سؤال الاختيار من متعدد التالي. يجب أن يكون السطر الأخير من إجابتك بالتنسيق التالي: 'ANSWER: [LETTER]' (بدون علامات اقتباس) حيث [LETTER] هو أحد الحروف A,B,C,D. فكّر خطوة بخطوة قبل الإجابة.\n\nأوجد درجة امتداد الحقل المحدد Q(sqrt(2)، sqrt(3)، sqrt(18)) على Q.\n\nA) 0\nB) 4\nC) 2\nD) 6"
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}
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],
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"choices": [
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"0",
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"4",
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"2",
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"6"
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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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"metadata": {
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"subject": "abstract_algebra",
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"language": "AR_XY"
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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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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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## 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 mmmlu \
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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=['mmmlu'],
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dataset_args={
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'mmmlu': {
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# subset_list: ['AR_XY', 'BN_BD', 'DE_DE'] # 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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