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license: cc-by-nc-sa-4.0
task_categories:
- visual-question-answering
- multiple-choice
language:
- en
pretty_name: HumaniBench-MCQ
size_categories:
- 1K<n<10K
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
# HumaniBench-MCQ: Task 3 (Multiple-Choice VQA) in MMMU Format
<p align="center">
<img src="https://github.com/user-attachments/assets/ebed8e26-5bdf-48c1-ae41-0775b8c33c0a" alt="HumaniBench Logo" height="120"/>
</p>
This dataset is a **format conversion** of Task 3 (`task3_Multiple_Choice_VQA`) from [HumaniBench](https://huggingface.co/datasets/vector-institute/HumaniBench), restructured to match the [MMMU](https://huggingface.co/datasets/MMMU/MMMU) schema so it can be used directly with MMMU-style evaluation pipelines.
**No questions, images, or answers were added, removed, or changed.** Only the column layout and answer format differ from the original.
<p align="center">
<a href="https://vectorinstitute.github.io/HumaniBench/"><img src="https://img.shields.io/badge/🌐-Website-4285F4" alt="Website"></a>
<a href="https://arxiv.org/abs/2505.11454"><img src="https://img.shields.io/badge/arXiv-Paper-b31b1b?logo=arxiv&logoColor=white" alt="Paper"></a>
<a href="https://github.com/VectorInstitute/HumaniBench"><img src="https://img.shields.io/badge/GitHub-Code-181717?logo=github" alt="Code"></a>
<a href="https://huggingface.co/datasets/vector-institute/HumaniBench"><img src="https://img.shields.io/badge/🤗-Original%20Dataset-FFD21F" alt="Original Dataset"></a>
</p>
---
## How to Load
```python
from datasets import load_dataset
ds = load_dataset("vector-institute/HumaniBench-MCQ", split="test")
```
---
## Dataset Structure
1,844 multiple-choice questions, one image each, in a single `test` split.
| Column | Type | Description |
|---|---|---|
| `id` | string | MMMU-style ID: `test_<Attribute>_<n>`, numbered within each attribute (e.g., `test_Gender_1`) |
| `question` | string | Question text, prefixed with the `<image 1>` placeholder |
| `options` | string | String-formatted list of answer choices, without letter prefixes (e.g., `['Male', 'Female', 'Non-binary', 'Gender-neutral']`) |
| `explanation` | string | Reasoning supporting the answer (optional; not required for evaluation) |
| `image_1` | image | Embedded image data |
| `img_type` | string | Image type, always `['Photographs']` |
| `answer` | string | Single-letter answer (e.g., `B`) |
| `demographic` | string | HumaniBench attribute being tested (e.g., Gender, Age). Used as the categorical field for grouping results (takes the place of MMMU's `topic_difficulty`) |
| `question_type` | string | Always `multiple-choice` |
| `subfield` | string | Left empty |
| `original_id` | string | ID from the original HumaniBench Task 3, for traceability |
### Example
```
id: test_Gender_2
question: <image 1> What gender representation is depicted by the statue of Lady Justice?
options: ['Male', 'Female', 'Non-binary', 'Gender-neutral']
answer: B
demographic: Gender
question_type: multiple-choice
original_id: c6caaf72e5
```
---
### Differences from MMMU
- Only `image_1` is included, since every question uses a single image. MMMU's `image_2` to `image_7` columns are omitted.
- MMMU's `topic_difficulty` column is replaced by `demographic`, which holds the attribute being tested.
- `original_id` is an extra column not present in MMMU.
---
## Intended Use
- Evaluating multimodal models on human-centric multiple-choice VQA using MMMU-compatible evaluation code
- Comparing model accuracy across social attributes via `demographic`
For the full benchmark (7 tasks, 32,000+ image–question pairs), see the [original HumaniBench dataset](https://huggingface.co/datasets/vector-institute/HumaniBench).
---
## Related Resources
- [Original HumaniBench Dataset](https://huggingface.co/datasets/vector-institute/HumaniBench)
- [Project Website](https://vectorinstitute.github.io/HumaniBench/)
- [GitHub Repository](https://github.com/VectorInstitute/HumaniBench)
- [Paper (arXiv)](https://arxiv.org/abs/2505.11454)
- [MMMU Dataset](https://huggingface.co/datasets/MMMU/MMMU) (format reference)
---
## License
This dataset is licensed under the **CC BY-NC-SA 4.0 License**, the same as the original HumaniBench.
---
## 🙏 Acknowledgments
Resources used in preparing this research were provided, in part, by the Province of Ontario, the Government of Canada through CIFAR, and companies sponsoring the Vector Institute ([vectorinstitute.ai/#partners](http://www.vectorinstitute.ai/#partners)).
This research was funded by the European Union's Horizon Europe research and innovation programme under the AIXPERT project (Grant Agreement No. 101214389), which aims to develop an agentic, multi-layered, GenAI-powered framework for creating explainable, accountable, and transparent AI systems.
---
## 📚 Citation
If you use this dataset, please cite the original HumaniBench paper:
```bibtex
@article{raza2026humanibench,
author = {Raza, Shaina and Narayanan, Aravind and Khazaie, Vahid Reza and Vayani, Ashmal and Radwan, Ahmed Y. and Chettiar, Mukund S. and Singh, Amandeep and Shah, Mubarak and Pandya, Deval},
title = {HumaniBench: A Human-Centric Framework for Large Multimodal Models Evaluation},
year = {2026},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
issn = {2157-6904},
url = {https://doi.org/10.1145/3845999},
doi = {10.1145/3845999},
note = {Just Accepted},
journal = {ACM Trans. Intell. Syst. Technol.},
month = sep,
}
``` |