| --- |
| 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`](https://huggingface.co/datasets/array/SAT) |
| prepared for upstream-friendly use with |
| [`EvolvingLMMs-Lab/lmms-eval`](https://github.com/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 |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
|
|
