license: cc-by-nc-4.0
task_categories:
- question-answering
- text-classification
- zero-shot-classification
- multiple-choice
tags:
- multi-choice
- question-answering
pretty_name: sata-bench-basic
size_categories:
- 1K<n<10K
Cite
@misc{xu2025satabenchselectapplybenchmark, title={SATA-BENCH: Select All That Apply Benchmark for Multiple Choice Questions}, author={Weijie Xu and Shixian Cui and Xi Fang and Chi Xue and Stephanie Eckman and Chandan Reddy}, year={2025}, eprint={2506.00643}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2506.00643}, }
Select-All-That-Apply Benchmark (SATA-bench) Dataset Desciption
SATA-Bench-raw is a multi-domain benchmark designed for 'Select-all-that-apply' questions. This dataset contains:
- Sata questions from several subjects, including reading, news, law, and biomedicine,
- ~8k questions with varying difficulty levels, multiple correct answers, and complex distractor options.
- Each question has more or equal to one correct answer and multiple distractors presented.
This dataset was designed to uncover selection bias of LLMs in multi-choice multi-answer setttings.
Warnings
This dataset is not labeled by human. The question from this dataset may be unclear. The dataset itself may contain incorrect answers. Please refer to sata-bench/sata_bench for a small subset of human labeled questions.