Datasets:
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README.md
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data_files:
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- split: train
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path: nn/train-*
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---
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data_files:
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- split: train
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path: nn/train-*
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license: mit
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task_categories:
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- question-answering
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language:
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- nb
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- nn
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pretty_name: NorCommonSenseQA
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size_categories:
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- n<1K
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---
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# Dataset Card for NorCommonSenseQA
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## Dataset Details
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### Dataset Description
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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.
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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.
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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:** [arxiv.org/abs/2501.11128](https://arxiv.org/abs/2501.11128) (to be presented at NoDaLiDa/Baltic-HLT 2025)
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- **License:** MIT
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### Uses
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NorCommonSenseQA is intended to be used for zero-shot evaluation of language models for Norwegian.
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## Dataset Creation
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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.
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<details>
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<summary><b>Stage 1: Human annotation and translation</b></summary>
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The annotation task here involves adapting the English examples from NorCommonSenseQA using two strategies.
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1. **Manual translation and localization**: The annotators manually translate the original examples, with localization that reflects Norwegian contexts where necessary.
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2. **Creative adaptation**: The annotators create new examples in Bokmål and Nynorsk from scratch, drawing inspiration from the shown English examples.
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</details>
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<details>
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<summary><b>Stage 2: Data Curation</b></summary>
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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.
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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.
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2. **Quality control**: The annotators judge spelling, grammar, and natural flow of an example, making minor edits if needed.
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</details>
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#### Personal and Sensitive Information
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The dataset does not contain information considered personal or sensitive.
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## Dataset Structure
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### Dataset Instances
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Each dataset instance looks as follows:
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#### Bokmål
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```
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{
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'id': 'ec882fc3a9bfaeae2a26fe31c2ef2c07',
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'question': 'Hvor plasserer man et pizzastykke før man spiser det?',
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'choices': {
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'label': ['A', 'B', 'C', 'D', 'E'],
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'text': [
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'På bordet',
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'På en tallerken',
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'På en restaurant',
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'I ovnen',
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'Populær'
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],
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},
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'answer': 'B',
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'curated': True
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}
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```
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#### Nynorsk
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```
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{
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'id': 'd35a8a3bd560fdd651ecf314878ed30f-78',
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'question': 'Viss du skulle ha steikt noko kjøt, kva ville du ha sett kjøtet på?',
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'choices': {
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'label': ['A', 'B', 'C', 'D', 'E'],
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'text': [
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'Olje',
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'Ein frysar',
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'Eit skinkesmørbrød',
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'Ein omn',
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'Ei steikepanne'
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]
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},
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'answer': 'E',
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'curated': False
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}
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```
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### Dataset Fields
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`id`: an example id \
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`question`: a question \
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`choices`: answer choices (`label`: a list of labels; `text`: a list of possible answers) \
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`answer`: the correct answer from the list of labels (A/B/C/D/E) \
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`curated`: an indicator of whether an example has been curated or not
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## Dataset Card Contact
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* Vladislav Mikhailov ([email protected])
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* Lilja Øvrelid ([email protected])
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