README.md

metadata

license: cc-by-nc-4.0
task_categories:
  - visual-question-answering
  - multiple-choice
language:
  - en
pretty_name: SAT (Spatial Aptitude Test)
size_categories:
  - n<1K
configs:
  - config_name: default
    data_files:
      - split: test
        path: data/test-*.parquet

SAT (Spatial Aptitude Test) — re-hosted for lmms-eval

This is a re-hosted copy of array/SAT prepared for upstream-friendly use with EvolvingLMMs-Lab/lmms-eval.

What changed vs. array/SAT

The original parquet uses a nested list<binary> schema for image bytes that fails pyarrow chunked-array conversion when load_dataset is called without streaming=True. lmms-eval's api/task.py calls load_dataset with num_proc=1, which is incompatible with streaming, so neither path works out-of-the-box.

The re-host fixes this by:

1. Storing images as Sequence(Image()) instead of raw nested binary — datasets handles the parquet encoding correctly and non-streaming loads succeed.
2. Pre-shuffling the answer order with a fixed seed (random.Random(42)) and storing correct_answer_idx directly. The upstream fork's api/task.py originally shuffled answer order at load time with a non-deterministic seed; baking this in makes the eval reproducible and removes the need for any framework patch.

All other fields (question, question_type, correct_answer) are passed through unchanged.

Schema

Field	Type	Description
image_bytes	Sequence(Image())	1 or 2 RGB JPEGs per item
question	Value("string")	Question text
answers	Sequence(Value("string"))	Two answer choices, shuffled seed=42
correct_answer_idx	Value("int32")	0 or 1, index into answers
correct_answer	Value("string")	Full text of the correct answer
question_type	Value("string")	One of obj_movement, ego_movement, action_conseq, perspective, goal_aim

Stats

• 150 test items, single test split
• Image count per item: 1 (104 items) or 2 (46 items)
• Answer count per item: 2 (binary MCQ)
• Question types: action_conseq 37, goal_aim 34, perspective 33, obj_movement 23, ego_movement 23

License

Inherits from the original array/SAT release. Use under the original licensing terms.

Citation

@article{ray2025sat,
  title={SAT: Dynamic Spatial Aptitude Training for Multimodal Language Models},
  author={Ray, Arijit and Duan, Jiafei and Tan, Reuben and Bashkirova, Dina and Hendrix, Rose and Ehsani, Kiana and Kembhavi, Aniruddha and Plummer, Bryan A and Krishna, Ranjay and Zeng, Kuo-Hao and others},
  journal={arXiv preprint arXiv:2412.07755},
  year={2025}
}