140 lines
3.3 KiB
Markdown
140 lines
3.3 KiB
Markdown
# ZeroBench
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## Overview
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ZeroBench is a challenging visual reasoning benchmark for Large Multimodal Models (LMMs). It consists of 100 high-quality, manually curated questions covering numerous domains, reasoning types, and image types designed to be beyond current model capabilities.
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## Task Description
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- **Task Type**: Advanced Visual Reasoning
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- **Input**: One or more images + challenging visual reasoning question
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- **Output**: Step-by-step reasoning with final answer in curly braces
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- **Domains**: Visual reasoning, perception, multi-step inference
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## Key Features
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- 100 manually curated high-quality questions
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- Designed to challenge frontier models (zero pass@1 with greedy decoding)
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- Covers diverse domains, reasoning types, and image types
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- No model achieves 5/5 reliability score
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- Tests limits of current visual reasoning capabilities
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## Evaluation Notes
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- Default evaluation uses the **zerobench** split
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- Primary metric: **Accuracy** with LLM judge
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- Answers must be in format: `{final answer}`
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- Includes subquestions split for detailed analysis
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- Uses image compression to handle large images
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## Properties
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| Property | Value |
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|----------|-------|
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| **Benchmark Name** | `zerobench` |
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| **Dataset ID** | [evalscope/zerobench](https://modelscope.cn/datasets/evalscope/zerobench/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** | `zerobench` |
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| **Train Split** | `zerobench_subquestions` |
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## Data Statistics
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| Metric | Value |
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|--------|-------|
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| Total Samples | 100 |
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| Prompt Length (Mean) | 645.72 chars |
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| Prompt Length (Min/Max) | 139 / 1998 chars |
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**Image Statistics:**
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| Metric | Value |
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|--------|-------|
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| Total Images | 108 |
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| Images per Sample | min: 1, max: 3, mean: 1.08 |
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| Resolution Range | 512x297 - 5559x4070 |
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| Formats | jpeg, png |
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## Sample Example
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**Subset**: `default`
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```json
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{
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"input": [
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{
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"id": "f3276b25",
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"content": [
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{
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"text": "I want to purchase all the Montellier bottles from the top three shelves. How much do I save by purchasing the bottles with a loyalty card? Give your final answer in dollars.\n\n\n\nLet's think step by step and give the final answer in curly braces,\nlike this: {final answer}\"\n"
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},
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{
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"image": "[BASE64_IMAGE: png, ~462.4KB]"
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}
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]
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}
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],
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"target": "11.90",
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"id": 0,
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"group_id": 0,
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"metadata": {
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"question_id": "1",
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"question_images": [
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"images/1_0.png"
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],
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"image_attribution": "Own"
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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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{question}
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Let's think step by step and give the final answer in curly braces,
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like this: {{final answer}}"
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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 zerobench \
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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=['zerobench'],
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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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