Datasets:
added an acl anthology reference
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README.md
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@@ -77,17 +77,30 @@ NorCommonSenseQA is part of the collection of Norwegian QA datasets, which also
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- **Curated by:** The [Language Technology Group](https://www.mn.uio.no/ifi/english/research/groups/ltg/) (LTG) at the University of Oslo
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- **Language:** Norwegian (Bokmål and Nynorsk)
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- **Repository:** [github.com/ltgoslo/norqa](https://github.com/ltgoslo/norqa)
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- **Paper:** [
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- **License:** MIT
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### Citation
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}
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```
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- **Curated by:** The [Language Technology Group](https://www.mn.uio.no/ifi/english/research/groups/ltg/) (LTG) at the University of Oslo
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- **Language:** Norwegian (Bokmål and Nynorsk)
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- **Repository:** [github.com/ltgoslo/norqa](https://github.com/ltgoslo/norqa)
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- **Paper:** [aclanthology.org/2025.nodalida-1.43](https://aclanthology.org/2025.nodalida-1.43) (NoDaLiDa/Baltic-HLT 2025)
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- **License:** MIT
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### Citation
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```
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@inproceedings{mikhailov-etal-2025-collection,
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title = "A Collection of Question Answering Datasets for {Norwegian}",
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author = "Mikhailov, Vladislav and
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M{\ae}hlum, Petter and
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Lang{\o}, Victoria Ovedie Chruickshank and
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Velldal, Erik and
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{\O}vrelid, Lilja",
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editor = "Johansson, Richard and
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Stymne, Sara",
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booktitle = "Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025)",
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month = mar,
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year = "2025",
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address = "Tallinn, Estonia",
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publisher = "University of Tartu Library",
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url = "https://aclanthology.org/2025.nodalida-1.43/",
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pages = "397--407",
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ISBN = "978-9908-53-109-0",
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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."
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}
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```
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