{
  "meta": {
    "pretty_name": "WorldVQA",
    "dataset_id": "evalscope/WorldVQA",
    "paper_url": null,
    "tags": [
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      "QA"
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    "eval_split": "train",
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      "Entertainment, Media & Gaming",
      "Brands, Logos & Graphic Design",
      "Sports, Gear & Venues"
    ],
    "description": "\n## Overview\n\nWorldVQA is a benchmark designed to evaluate the atomic visual world knowledge of Multimodal Large Language Models (MLLMs). It measures models' ability to ground and name visual entities across a stratified taxonomy, spanning from common head-class objects to long-tail rarities.\n\n## Task Description\n\n- **Task Type**: Visual Entity Recognition / Knowledge QA\n- **Input**: Image + question asking to identify a visual entity\n- **Output**: Free-form text answer (specific entity name)\n- **Domain**: Nature, architecture, culture, products, transportation, entertainment, brands, sports\n\n## Key Features\n\n- 3000 VQA pairs across 8 semantic categories\n- Bilingual: English (non-zh) and Chinese (zh)\n- Three difficulty levels: easy, medium, hard\n- Tests atomic visual knowledge decoupled from reasoning\n- Requires precise entity identification (e.g., specific breed, not generic \"dog\")\n\n## Evaluation Notes\n\n- Default configuration uses **0-shot** evaluation\n- Evaluates on **train** split (the benchmark data split)\n- Primary metric: **Accuracy** via LLM-as-judge\n- Supports LLM judge for semantic equivalence checking\n- Results reported per category and overall\n",
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    "category": "vlm"
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        {
          "id": "0c08c4e2",
          "content": [
            {
              "text": "What breed of dog is in the picture?"
            },
            {
              "image": "[BASE64_IMAGE: png, ~392.9KB]"
            }
          ]
        }
      ],
      "target": "Greek Hound",
      "id": 0,
      "group_id": 0,
      "subset_key": "Nature & Environment",
      "metadata": {
        "index": 0,
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        "language": "non-zh",
        "difficulty": "medium"
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  "readme": {
    "en": "# WorldVQA\n\n\n## Overview\n\nWorldVQA is a benchmark designed to evaluate the atomic visual world knowledge of Multimodal Large Language Models (MLLMs). It measures models' ability to ground and name visual entities across a stratified taxonomy, spanning from common head-class objects to long-tail rarities.\n\n## Task Description\n\n- **Task Type**: Visual Entity Recognition / Knowledge QA\n- **Input**: Image + question asking to identify a visual entity\n- **Output**: Free-form text answer (specific entity name)\n- **Domain**: Nature, architecture, culture, products, transportation, entertainment, brands, sports\n\n## Key Features\n\n- 3000 VQA pairs across 8 semantic categories\n- Bilingual: English (non-zh) and Chinese (zh)\n- Three difficulty levels: easy, medium, hard\n- Tests atomic visual knowledge decoupled from reasoning\n- Requires precise entity identification (e.g., specific breed, not generic \"dog\")\n\n## Evaluation Notes\n\n- Default configuration uses **0-shot** evaluation\n- Evaluates on **train** split (the benchmark data split)\n- Primary metric: **Accuracy** via LLM-as-judge\n- Supports LLM judge for semantic equivalence checking\n- Results reported per category and overall\n\n\n## Properties\n\n| Property | Value |\n|----------|-------|\n| **Benchmark Name** | `world_vqa` |\n| **Dataset ID** | [evalscope/WorldVQA](https://modelscope.cn/datasets/evalscope/WorldVQA/summary) |\n| **Paper** | N/A |\n| **Tags** | `Knowledge`, `MultiModal`, `QA` |\n| **Metrics** | `acc` |\n| **Default Shots** | 0-shot |\n| **Evaluation Split** | `train` |\n\n\n## Data Statistics\n\n| Metric | Value |\n|--------|-------|\n| Total Samples | 3,000 |\n| Prompt Length (Mean) | 38.4 chars |\n| Prompt Length (Min/Max) | 5 / 182 chars |\n\n**Per-Subset Statistics:**\n\n| Subset | Samples | Prompt Mean | Prompt Min | Prompt Max |\n|--------|---------|-------------|------------|------------|\n| `Nature & Environment` | 326 | 33.9 | 7 | 83 |\n| `Locations & Architecture` | 512 | 42.03 | 9 | 122 |\n| `Culture, Arts & Crafts` | 506 | 33.71 | 7 | 112 |\n| `Objects & Products` | 437 | 55.97 | 9 | 182 |\n| `Vehicles, Craft & Transportation` | 306 | 30.67 | 8 | 114 |\n| `Entertainment, Media & Gaming` | 511 | 30.5 | 5 | 83 |\n| `Brands, Logos & Graphic Design` | 260 | 39.1 | 6 | 83 |\n| `Sports, Gear & Venues` | 142 | 41.99 | 9 | 100 |\n\n**Image Statistics:**\n\n| Metric | Value |\n|--------|-------|\n| Total Images | 3,000 |\n| Images per Sample | min: 1, max: 1, mean: 1 |\n| Resolution Range | 33x65 - 3840x3840 |\n| Formats | gif, jpeg, png, webp |\n\n\n## Sample Example\n\n**Subset**: `Nature & Environment`\n\n```json\n{\n  \"input\": [\n    {\n      \"id\": \"0c08c4e2\",\n      \"content\": [\n        {\n          \"text\": \"What breed of dog is in the picture?\"\n        },\n        {\n          \"image\": \"[BASE64_IMAGE: png, ~392.9KB]\"\n        }\n      ]\n    }\n  ],\n  \"target\": \"Greek Hound\",\n  \"id\": 0,\n  \"group_id\": 0,\n  \"subset_key\": \"Nature & Environment\",\n  \"metadata\": {\n    \"index\": 0,\n    \"category\": \"Nature & Environment\",\n    \"language\": \"non-zh\",\n    \"difficulty\": \"medium\"\n  }\n}\n```\n\n## Prompt Template\n\n**Prompt Template:**\n```text\n{question}\n```\n\n## Usage\n\n### Using CLI\n\n```bash\nevalscope eval \\\n    --model YOUR_MODEL \\\n    --api-url OPENAI_API_COMPAT_URL \\\n    --api-key EMPTY_TOKEN \\\n    --datasets world_vqa \\\n    --limit 10  # Remove this line for formal evaluation\n```\n\n### Using Python\n\n```python\nfrom evalscope import run_task\nfrom evalscope.config import TaskConfig\n\ntask_cfg = TaskConfig(\n    model='YOUR_MODEL',\n    api_url='OPENAI_API_COMPAT_URL',\n    api_key='EMPTY_TOKEN',\n    datasets=['world_vqa'],\n    dataset_args={\n        'world_vqa': {\n            # subset_list: ['Nature & Environment', 'Locations & Architecture', 'Culture, Arts & Crafts']  # optional, evaluate specific subsets\n        }\n    },\n    limit=10,  # Remove this line for formal evaluation\n)\n\nrun_task(task_cfg=task_cfg)\n```\n\n\n",
    "zh": "# WorldVQA\n\n\n## 概述\n\nWorldVQA 是一个用于评估多模态大语言模型（MLLMs）原子级视觉世界知识的基准测试。它衡量模型在分层分类体系中对视觉实体进行定位和命名的能力，涵盖从常见的头部类别物体到长尾稀有类别的广泛范围。\n\n## 任务描述\n\n- **任务类型**：视觉实体识别 / 知识问答（QA）\n- **输入**：图像 + 要求识别视觉实体的问题\n- **输出**：自由文本答案（具体实体名称）\n- **领域**：自然、建筑、文化、产品、交通、娱乐、品牌、体育\n\n## 主要特点\n\n- 包含 3000 个 VQA 对，覆盖 8 个语义类别\n- 双语支持：英语（non-zh）和中文（zh）\n- 三个难度等级：简单、中等、困难\n- 测试与推理解耦的原子级视觉知识\n- 要求精确识别实体（例如，具体品种，而非泛称“狗”）\n\n## 评估说明\n\n- 默认配置采用 **0-shot** 评估\n- 在 **train** 划分上进行评估（即基准测试数据划分）\n- 主要指标：通过 LLM-as-judge 计算的 **准确率（Accuracy）**\n- 支持使用 LLM 判定语义等价性\n- 结果按类别及整体分别报告\n\n## 属性\n\n| 属性 | 值 |\n|----------|-------|\n| **基准测试名称** | `world_vqa` |\n| **数据集ID** | [evalscope/WorldVQA](https://modelscope.cn/datasets/evalscope/WorldVQA/summary) |\n| **论文** | N/A |\n| **标签** | `Knowledge`, `MultiModal`, `QA` |\n| **指标** | `acc` |\n| **默认示例数** | 0-shot |\n| **评估划分** | `train` |\n\n\n## 数据统计\n\n| 指标 | 值 |\n|--------|-------|\n| 总样本数 | 3,000 |\n| 提示词长度（平均） | 38.4 字符 |\n| 提示词长度（最小/最大） | 5 / 182 字符 |\n\n**各子集统计信息：**\n\n| 子集 | 样本数 | 提示词平均长度 | 提示词最小长度 | 提示词最大长度 |\n|--------|---------|-------------|------------|------------|\n| `Nature & Environment` | 326 | 33.9 | 7 | 83 |\n| `Locations & Architecture` | 512 | 42.03 | 9 | 122 |\n| `Culture, Arts & Crafts` | 506 | 33.71 | 7 | 112 |\n| `Objects & Products` | 437 | 55.97 | 9 | 182 |\n| `Vehicles, Craft & Transportation` | 306 | 30.67 | 8 | 114 |\n| `Entertainment, Media & Gaming` | 511 | 30.5 | 5 | 83 |\n| `Brands, Logos & Graphic Design` | 260 | 39.1 | 6 | 83 |\n| `Sports, Gear & Venues` | 142 | 41.99 | 9 | 100 |\n\n**图像统计信息：**\n\n| 指标 | 值 |\n|--------|-------|\n| 总图像数 | 3,000 |\n| 每样本图像数 | 最小: 1, 最大: 1, 平均: 1 |\n| 分辨率范围 | 33x65 - 3840x3840 |\n| 格式 | gif, jpeg, png, webp |\n\n\n## 样例示例\n\n**子集**: `Nature & Environment`\n\n```json\n{\n  \"input\": [\n    {\n      \"id\": \"0c08c4e2\",\n      \"content\": [\n        {\n          \"text\": \"What breed of dog is in the picture?\"\n        },\n        {\n          \"image\": \"[BASE64_IMAGE: png, ~392.9KB]\"\n        }\n      ]\n    }\n  ],\n  \"target\": \"Greek Hound\",\n  \"id\": 0,\n  \"group_id\": 0,\n  \"subset_key\": \"Nature & Environment\",\n  \"metadata\": {\n    \"index\": 0,\n    \"category\": \"Nature & Environment\",\n    \"language\": \"non-zh\",\n    \"difficulty\": \"medium\"\n  }\n}\n```\n\n## 提示模板\n\n**提示模板：**\n```text\n{question}\n```\n\n## 使用方法\n\n### 使用 CLI\n\n```bash\nevalscope eval \\\n    --model YOUR_MODEL \\\n    --api-url OPENAI_API_COMPAT_URL \\\n    --api-key EMPTY_TOKEN \\\n    --datasets world_vqa \\\n    --limit 10  # 正式评估时请删除此行\n```\n\n### 使用 Python\n\n```python\nfrom evalscope import run_task\nfrom evalscope.config import TaskConfig\n\ntask_cfg = TaskConfig(\n    model='YOUR_MODEL',\n    api_url='OPENAI_API_COMPAT_URL',\n    api_key='EMPTY_TOKEN',\n    datasets=['world_vqa'],\n    dataset_args={\n        'world_vqa': {\n            # subset_list: ['Nature & Environment', 'Locations & Architecture', 'Culture, Arts & Crafts']  # 可选，用于评估特定子集\n        }\n    },\n    limit=10,  # 正式评估时请删除此行\n)\n\nrun_task(task_cfg=task_cfg)\n```",
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  "updated_at": "2026-07-02T19:25:12.649613",
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