| | # VisToolBench Dataset |
| |
|
| | A benchmark dataset for evaluating vision-language models on tool-use tasks. |
| |
|
| | ## Dataset Statistics |
| |
|
| | - **Total samples**: 1204 |
| | - **Single-turn**: 603 |
| | - **Multi-turn**: 601 |
| |
|
| | ## Schema |
| |
|
| | | Column | Type | Description | |
| | |--------|------|-------------| |
| | | `id` | string | Unique task identifier | |
| | | `turncase` | string | Either "single-turn" or "multi-turn" | |
| | | `num_turns` | int | Number of conversation turns (1 for single-turn) | |
| | | `prompt_category` | string | Task category (e.g., "medical", "scientific", "general") | |
| | | `eval_focus` | string | What aspect is being evaluated (e.g., "visual_reasoning", "tool_use") | |
| | | `prompt` | string | The user prompt/question. For multi-turn, turns are prefixed with `[Turn N]` | |
| | | `golden_answer` | string | The reference/ground-truth answer | |
| | | `image` | Image | Primary image for the task (displayed in HF viewer) | |
| | | `images` | List[Image] | All images associated with the task | |
| | | `num_images` | int | Total number of images | |
| | | `tool_trajectory` | string | JSON string of tool calls made (if applicable) | |
| | | `rubrics` | string | JSON string of evaluation rubrics with weights and metadata | |
| |
|
| | ## Rubrics Format |
| |
|
| | Each rubric entry contains: |
| | - `description`: What the rubric evaluates |
| | - `weight`: Importance weight (1-5) |
| | - `objective/subjective`: Whether evaluation is objective or subjective |
| | - `explicit/implicit`: Whether the answer is explicit or implicit in the image |
| | - `category`: List of categories (e.g., "instruction following", "truthfulness") |
| | - `critical`: Whether this is a critical rubric ("yes"/"no") |
| | - `final_answer`: Whether this relates to the final answer ("yes"/"no") |
| |
|
| | ## Usage |
| |
|
| | ```python |
| | from datasets import load_dataset |
| | |
| | # Load the dataset |
| | ds = load_dataset("path/to/dataset") |
| | |
| | # Access a sample |
| | sample = ds['train'][0] |
| | print(sample['prompt']) |
| | print(sample['image']) # PIL Image |
| | |
| | # Parse rubrics |
| | import json |
| | rubrics = json.loads(sample['rubrics']) |
| | for rubric_id, rubric in rubrics.items(): |
| | print(f"{rubric['description']} (weight: {rubric['weight']})") |
| | ``` |
| |
|
| | ## Splits |
| |
|
| | - `train`: Full dataset (1204 samples) |
| |
|
| | ## Citation |
| |
|
| | ``` |
| | @article{guo2025beyond, |
| | title={Beyond seeing: Evaluating multimodal llms on tool-enabled image perception, transformation, and reasoning}, |
| | author={Guo, Xingang and Tyagi, Utkarsh and Gosai, Advait and Vergara, Paula and Park, Jayeon and Montoya, Ernesto Gabriel Hern{\'a}ndez and Zhang, Chen Bo Calvin and Hu, Bin and He, Yunzhong and Liu, Bing and others}, |
| | journal={arXiv preprint arXiv:2510.12712}, |
| | year={2025} |
| | } |
| | ``` |
| |
|
