--- dataset_info: - config_name: nb features: - name: id dtype: string - name: question dtype: string - name: choices struct: - name: label sequence: string - name: text sequence: string - name: answer dtype: string - name: curated dtype: bool splits: - name: train num_bytes: 219533 num_examples: 998 download_size: 128333 dataset_size: 219533 - config_name: nn features: - name: id dtype: string - name: question dtype: string - name: choices struct: - name: label sequence: string - name: text sequence: string - name: answer dtype: string - name: curated dtype: bool splits: - name: train num_bytes: 24664 num_examples: 95 download_size: 20552 dataset_size: 24664 configs: - config_name: nb data_files: - split: train path: nb/train-* - config_name: nn data_files: - split: train path: nn/train-* license: mit task_categories: - question-answering language: - nb - nn pretty_name: NorCommonSenseQA size_categories: - n<1K --- # Dataset Card for NorCommonSenseQA ## Dataset Details ### Dataset Description NorCommonSenseQA is a multiple-choice question answering (QA) dataset designed for zero-shot evaluation of language models' commonsense reasoning abilities. NorCommonSenseQA counts 1093 examples in both written standards of Norwegian: Bokmål and Nynorsk (the minority variant). Each example consists of a question and five answer choices. NorCommonSenseQA is part of the collection of Norwegian QA datasets, which also includes: [NRK-Quiz-QA](https://huggingface.co/datasets/ltg/nrk_quiz_qa), [NorOpenBookQA](https://huggingface.co/datasets/ltg/noropenbookqa), [NorTruthfulQA (Multiple Choice)](https://huggingface.co/datasets/ltg/nortruthfulqa_mc), and [NorTruthfulQA (Generation)](https://huggingface.co/datasets/ltg/nortruthfulqa_gen). We describe our high-level dataset creation approach here and provide more details, general statistics, and model evaluation results in our paper. - **Curated by:** The [Language Technology Group](https://www.mn.uio.no/ifi/english/research/groups/ltg/) (LTG) at the University of Oslo - **Language:** Norwegian (Bokmål and Nynorsk) - **Repository:** [github.com/ltgoslo/norqa](https://github.com/ltgoslo/norqa) - **Paper:** [aclanthology.org/2025.nodalida-1.43](https://aclanthology.org/2025.nodalida-1.43) (NoDaLiDa/Baltic-HLT 2025) - **License:** MIT ### Citation ``` @inproceedings{mikhailov-etal-2025-collection, title = "A Collection of Question Answering Datasets for {Norwegian}", author = "Mikhailov, Vladislav and M{\ae}hlum, Petter and Lang{\o}, Victoria Ovedie Chruickshank and Velldal, Erik and {\O}vrelid, Lilja", editor = "Johansson, Richard and Stymne, Sara", booktitle = "Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025)", month = mar, year = "2025", address = "Tallinn, Estonia", publisher = "University of Tartu Library", url = "https://aclanthology.org/2025.nodalida-1.43/", pages = "397--407", ISBN = "978-9908-53-109-0", abstract = "This paper introduces a new suite of question answering datasets for Norwegian; NorOpenBookQA, NorCommonSenseQA, NorTruthfulQA, and NRK-Quiz-QA. The data covers a wide range of skills and knowledge domains, including world knowledge, commonsense reasoning, truthfulness, and knowledge about Norway. Covering both of the written standards of Norwegian {--} Bokm{\r{a}}l and Nynorsk {--} our datasets comprise over 10k question-answer pairs, created by native speakers. We detail our dataset creation approach and present the results of evaluating 11 language models (LMs) in zero- and few-shot regimes. Most LMs perform better in Bokm{\r{a}}l than Nynorsk, struggle most with commonsense reasoning, and are often untruthful in generating answers to questions. All our datasets and annotation materials are publicly available." } ``` ### Uses NorCommonSenseQA is intended to be used for zero-shot evaluation of language models for Norwegian. ## Dataset Creation NorCommonSenseQA is created by adapting the [CommonSenseQA](https://huggingface.co/datasets/tau/commonsense_qa) dataset for English via a two-stage annotation. Our annotation team consists of 21 BA/BSc and MA/MSc students in linguistics and computer science, all native Norwegian speakers. The team is divided into two groups: 19 annotators focus on Bokmål, while two annotators work on Nynorsk.
Stage 1: Human annotation and translation The annotation task here involves adapting the English examples from NorCommonSenseQA using two strategies. 1. **Manual translation and localization**: The annotators manually translate the original examples, with localization that reflects Norwegian contexts where necessary. 2. **Creative adaptation**: The annotators create new examples in Bokmål and Nynorsk from scratch, drawing inspiration from the shown English examples.
Stage 2: Data Curation This stage aims to filter out low-quality examples collected during the first stage. Due to resource constraints, we have curated 91% of the examples (998 out of 1093), with each example validated by a single annotator. Each annotator receives pairs of the original and translated/localized examples or newly created examples for review. The annotation task here involves two main steps. 1. **Quality judgment**: The annotators judge the overall quality of an example and label any example that is of low quality or requires a substantial revision. Examples like this are not included in our datasets. 2. **Quality control**: The annotators judge spelling, grammar, and natural flow of an example, making minor edits if needed.
#### Personal and Sensitive Information The dataset does not contain information considered personal or sensitive. ## Dataset Structure ### Dataset Instances Each dataset instance looks as follows: #### Bokmål ``` { 'id': 'ec882fc3a9bfaeae2a26fe31c2ef2c07', 'question': 'Hvor plasserer man et pizzastykke før man spiser det?', 'choices': { 'label': ['A', 'B', 'C', 'D', 'E'], 'text': [ 'På bordet', 'På en tallerken', 'På en restaurant', 'I ovnen', 'Populær' ], }, 'answer': 'B', 'curated': True } ``` #### Nynorsk ``` { 'id': 'd35a8a3bd560fdd651ecf314878ed30f-78', 'question': 'Viss du skulle ha steikt noko kjøt, kva ville du ha sett kjøtet på?', 'choices': { 'label': ['A', 'B', 'C', 'D', 'E'], 'text': [ 'Olje', 'Ein frysar', 'Eit skinkesmørbrød', 'Ein omn', 'Ei steikepanne' ] }, 'answer': 'E', 'curated': False } ``` ### Dataset Fields `id`: an example id \ `question`: a question \ `choices`: answer choices (`label`: a list of labels; `text`: a list of possible answers) \ `answer`: the correct answer from the list of labels (A/B/C/D/E) \ `curated`: an indicator of whether an example has been curated or not ## Dataset Card Contact * Vladislav Mikhailov (vladism@ifi.uio.no) * Lilja Øvrelid (liljao@ifi.uio.no)