--- dataset_info: features: - name: id dtype: string - name: instance_id dtype: int64 - name: question dtype: string - name: answer list: dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: category dtype: string - name: img dtype: image configs: - config_name: 1_correct data_files: - split: validation path: "1_correct/validation/0000.parquet" - split: test path: "1_correct/test/0000.parquet" - config_name: 1_correct_var data_files: - split: validation path: "1_correct_var/validation/0000.parquet" - split: test path: "1_correct_var/test/0000.parquet" - config_name: n_correct data_files: - split: validation path: "n_correct/validation/0000.parquet" - split: test path: "n_correct/test/0000.parquet" --- # DARE DARE (Diverse Visual Question Answering with Robustness Evaluation) is a carefully created and curated multiple-choice VQA benchmark. DARE evaluates VLM performance on five diverse categories and includes four robustness-oriented evaluations based on the variations of: - prompts - the subsets of answer options - the output format - the number of correct answers. The validation split of the dataset contains images, questions, answer options, and correct answers. We are not publishing the correct answers for the test split to prevent contamination. ## Load the Dataset To use the dataset use the huggingface datasets library: ``` from datasets import load_dataset # Load the dataset subset = "1_correct" # Change to the subset that you want to use dataset = load_dataset("cambridgeltl/DARE", subset) ``` ## Citation If you use this dataset, please cite our paper: ``` @article{sterz2024dare, title={DARE: Diverse Visual Question Answering with Robustness Evaluation}, author={Sterz, Hannah and Pfeiffer, Jonas and Vuli{\'c}, Ivan}, journal={arXiv preprint arXiv:2409.18023}, year={2024} } ```