154 lines
4.5 KiB
Markdown
154 lines
4.5 KiB
Markdown
# MMBench
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## Overview
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MMBench is a systematically designed benchmark for evaluating vision-language models across 20 fine-grained ability dimensions. It uses a novel CircularEval strategy and provides both English and Chinese versions for cross-lingual evaluation.
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## Task Description
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- **Task Type**: Visual Multiple-Choice Q&A
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- **Input**: Image with question and 2-4 answer options
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- **Output**: Single correct answer letter (A, B, C, or D)
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- **Languages**: English (en) and Chinese (cn) subsets
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## Key Features
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- 20 fine-grained ability dimensions defined
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- Systematic evaluation pipeline with CircularEval strategy
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- Bilingual support (English and Chinese)
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- Questions include hints for context
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- Tests perception, reasoning, and knowledge abilities
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## Evaluation Notes
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- Default configuration uses **0-shot** evaluation
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- Uses Chain-of-Thought (CoT) prompting
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- Evaluates on dev split
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- Two subsets: `cn` (Chinese) and `en` (English)
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- Results include category-level breakdown
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## Properties
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| Property | Value |
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|----------|-------|
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| **Benchmark Name** | `mm_bench` |
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| **Dataset ID** | [lmms-lab/MMBench](https://modelscope.cn/datasets/lmms-lab/MMBench/summary) |
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| **Paper** | N/A |
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| **Tags** | `Knowledge`, `MultiModal`, `QA` |
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| **Metrics** | `acc` |
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| **Default Shots** | 0-shot |
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| **Evaluation Split** | `dev` |
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## Data Statistics
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| Metric | Value |
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|--------|-------|
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| Total Samples | 8,658 |
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| Prompt Length (Mean) | 372.3 chars |
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| Prompt Length (Min/Max) | 241 / 2395 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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| `cn` | 4,329 | 312.28 | 241 | 1681 |
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| `en` | 4,329 | 432.33 | 256 | 2395 |
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**Image Statistics:**
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| Metric | Value |
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|--------|-------|
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| Total Images | 8,658 |
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| Images per Sample | min: 1, max: 1, mean: 1 |
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| Resolution Range | 106x56 - 512x512 |
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| Formats | jpeg |
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## Sample Example
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**Subset**: `cn`
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```json
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{
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"input": [
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{
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"id": "00b49734",
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"content": [
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{
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"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 A,B. Think step by step before answering.\n\n下面的文章描述了一个实验。阅读文章，然后按照以下说明进行操作。\n\nMadelyn在雪板的底部涂上了一层薄蜡，然后直接下坡滑行。然后，她去掉了蜡，再次直接下坡滑行。她重复了这个过程四次，每次都交替使用薄蜡或不使用薄蜡滑行。她的朋友Tucker计时每次滑行的时间。Madelyn和Tucker计算了使用薄蜡滑行和不使用薄蜡滑行时直接下坡所需的平均时间。\n图：滑雪板下坡。麦德琳和塔克的实验能最好回答哪个问题？\n\nA) 当麦德琳的雪板上有一层薄蜡或一层厚蜡时，它是否能在较短的时间内滑下山坡？\nB) 当麦德琳的雪板上有一层蜡或没有蜡时，它是否能在较短的时间内滑下山坡？"
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},
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{
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"image": "[BASE64_IMAGE: jpeg, ~11.7KB]"
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}
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]
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}
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],
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"choices": [
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"当麦德琳的雪板上有一层薄蜡或一层厚蜡时，它是否能在较短的时间内滑下山坡？",
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"当麦德琳的雪板上有一层蜡或没有蜡时，它是否能在较短的时间内滑下山坡？"
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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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"index": 241,
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"category": "identity_reasoning",
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"source": "scienceqa",
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"L2-category": "attribute_reasoning",
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"comment": "nan",
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"split": "dev"
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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 mm_bench \
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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=['mm_bench'],
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dataset_args={
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'mm_bench': {
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# subset_list: ['cn', 'en'] # 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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