Duplicate from WestlakeNLP/FigureBench

Co-authored-by: MinjunZhu <MinjunZhu@users.noreply.huggingface.co>

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README.md

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+---
+license: cc-by-4.0
+task_categories:
+- text-to-image
+configs:
+- config_name: default
+  data_files:
+  - split: dev
+    path: data/dev.parquet
+  - split: test
+    path: data/test.parquet
+---
+
+# FigureBench
+
+**The first large-scale benchmark for generating scientific illustrations from long-form scientific texts.**
+
+[Paper](https://huggingface.co/papers/2602.03828) | [Code](https://github.com/ResearAI/AutoFigure)
+
+## Overview
+
+FigureBench is curated to encompass a wide array of document types, including research papers, surveys, technical blogs, and textbooks, establishing a challenging and diverse testbed to spur research in automatic scientific illustration generation.
+
+This dataset contains:
+- **Development Set (dev)**: 3,000 samples with conversation-format annotations for training and development
+- **Test Set (test)**: 300 samples with simple text-image pairs **strictly for evaluation**
+
+## HuggingFace
+
+The dataset is available on HuggingFace: [WestlakeNLP/FigureBench](https://huggingface.co/datasets/WestlakeNLP/FigureBench)
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("WestlakeNLP/FigureBench")
+
+# Access development set (for training/development)
+dev_sample = dataset["dev"][0]
+
+# Access test set (for evaluation only)
+test_sample = dataset["test"][0]
+```
+
+## Dataset Statistics
+
+| Category | Dev Samples | Test Samples | Avg. Text Tokens | Text Density (%) |
+|----------|-------------|--------------|------------------|------------------|
+| Paper | 2,900 | 200 | 12,732 | 42.1 |
+| Blog | 20 | 20 | 4,047 | 46.0 |
+| Survey | 40 | 40 | 2,179 | 43.8 |
+| Textbook | 40 | 40 | 352 | 25.0 |
+| **Total** | **3,000** | **300** | - | - |
+
+## Key Challenges
+
+- **Long-context reasoning**: Text tokens vary by over an order of magnitude (352 for Textbooks to 12,732 for Papers)
+- **High text density**: Average 41.2% of image area occupied by text
+- **Structural complexity**: Average 5.3 components and 6.4 shapes per illustration
+- **Visual richness**: Average 6.2 colors per illustration
+
+## Data Format
+
+### Development Set (dev)
+
+Each sample in the development set uses a conversation format:
+
+```json
+{
+    "messages": [
+        {
+            "role": "system",
+            "content": "You are an expert in analyzing scientific papers..."
+        },
+        {
+            "role": "user",
+            "content": "Please determine whether the image is the most important illustration...
+
+[Paper text content]"
+        },
+        {
+            "role": "assistant",
+            "content": "yes"
+        }
+    ],
+    "images": ["images/{paper_id}/{image_filename}"]
+}
+```
+
+### Test Set (test)
+
+The test set uses a simple text-image pair format for evaluation:
+
+```json
+{
+    "text": "Original document text content...",
+    "image": "test_images/{category}/{filename}.png",
+    "category": "paper|survey|blog|textbook"
+}
+```
+
+| Field | Type | Description |
+|-------|------|-------------|
+| text | string | Original document text (LaTeX for papers, Markdown for blogs/surveys, plain text for textbooks) |
+| image | string | Relative path to the figure image |
+| category | string | Document category: paper, survey, blog, or textbook |
+
+## Usage
+
+### Loading Development Set
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("WestlakeNLP/FigureBench")
+dev = dataset["dev"]
+
+sample = dev[0]
+messages = sample["messages"]
+image_path = sample["images"][0]
+```
+
+### Loading Test Set for Evaluation
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("WestlakeNLP/FigureBench")
+test = dataset["test"]
+
+sample = test[0]
+text = sample["text"]
+image_path = sample["image"]
+category = sample["category"]
+```
+
+## Dataset Structure
+
+```
+FigureBench/
+├── README.md
+├── data/
+│   ├── dev.parquet      # Development set (3,000 samples)
+│   └── test.parquet     # Test set (300 samples)
+├── images/              # Development set images
+│   ├── {paper_id}/
+│   │   └── {paper_id}_{figure_name}.png
+│   └── ...
+└── test_images/         # Test set images
+    ├── blog/
+    ├── paper/
+    ├── survey/
+    └── textbook/
+```
+
+## Data Curation
+
+### Source Data
+
+The dataset is curated from multiple high-quality sources:
+
+- **Papers**: Sampled from the Research-14K dataset, filtered using GPT to select illustrations that best represent core methodologies
+- **Surveys**: Structural diagrams (roadmaps, taxonomies) from recent AI surveys on arXiv
+- **Textbooks**: Sourced from open-licensed educational platforms (e.g., OpenStax) for pedagogical clarity
+- **Blogs**: Hand-collected from technical outlets (e.g., ICLR Blog Track) for modern visual styles
+
+### Annotation Process
+
+1. Initial filtering to retain only conceptual illustrations (excluding data-driven charts)
+2. Ensured each key visual element is explicitly described in the source text
+3. Two independent annotators evaluated each pair
+4. Only pairs approved by both annotators were included
+5. Achieved high Inter-Rater Reliability (IRR) of 0.91
+
+## Citation
+
+If you use FigureBench or AutoFigure in your research, please cite:
+
+```bibtex
+@inproceedings{
+zhu2026autofigure,
+title={AutoFigure: Generating and Refining Publication-Ready Scientific Illustrations},
+author={Minjun Zhu and Zhen Lin and Yixuan Weng and Panzhong Lu and Qiujie Xie and Yifan Wei and Sifan Liu and Qiyao Sun and Yue Zhang},
+booktitle={The Fourteenth International Conference on Learning Representations},
+year={2026},
+url={https://openreview.net/forum?id=5N3z9JQJKq}
+}
+```
+
+## License
+
+This dataset is released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).
+
+The curation process adhered to open-source licenses. Please refer to the original papers for the copyright of individual figures.

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