Datasets:

vector-institute
/

HumaniBench-MCQ

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

HumaniBench Logo

This dataset is a format conversion of Task 3 (task3_Multiple_Choice_VQA) from HumaniBench, restructured to match the 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.

How to Load

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.

Related Resources

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).

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:

@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,
}