Datasets:

ltg
/

noropenbookqa

dataset_info:
  - config_name: nb
    features:
      - name: id
        dtype: string
      - name: question_stem
        dtype: string
      - name: choices
        struct:
          - name: label
            sequence: string
          - name: text
            sequence: string
      - name: answer
        dtype: string
      - name: fact
        dtype: string
      - name: curated
        dtype: bool
    splits:
      - name: train
        num_bytes: 691423
        num_examples: 2886
      - name: test
        num_bytes: 89887
        num_examples: 376
    download_size: 445496
    dataset_size: 781310
  - config_name: nn
    features:
      - name: id
        dtype: string
      - name: question_stem
        dtype: string
      - name: choices
        struct:
          - name: label
            sequence: string
          - name: text
            sequence: string
      - name: answer
        dtype: string
      - name: fact
        dtype: string
      - name: curated
        dtype: bool
    splits:
      - name: train
        num_bytes: 43819
        num_examples: 163
      - name: test
        num_bytes: 23397
        num_examples: 90
    download_size: 50213
    dataset_size: 67216
configs:
  - config_name: nb
    data_files:
      - split: train
        path: nb/train-*
      - split: test
        path: nb/test-*
  - config_name: nn
    data_files:
      - split: train
        path: nn/train-*
      - split: test
        path: nn/test-*
license: mit
task_categories:
  - question-answering
language:
  - nb
  - nn
pretty_name: NorOpenBookQA
size_categories:
  - 1K<n<10K

Dataset Card for NorOpenBookQA

Dataset Details

Dataset Description

NorOpenBookQA is a multiple-choice question answering (QA) dataset designed for zero- and few-shot evaluation of language models' world knowledge. NorOpenBookQA counts 3.5k examples in both written standards of Norwegian: Bokmål and Nynorsk (the minority variant). Each example consists of an elementary-level science question, four answer choices, and a factual statement that presents the evidence necessary to determine the correct answer. Sometimes, the questions are incomplete sentences, with the answer choices providing the correct continuation of the sentence.

NorOpenBookQA is part of the collection of Norwegian QA datasets, which also includes NRK-Quiz-QA, NorCommonSenseQA, NorTruthfulQA (Multiple Choice), and NorTruthfulQA (Generation). 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 (LTG) at the University of Oslo
Language: Norwegian (Bokmål and Nynorsk)
Repository: github.com/ltgoslo/norqa
Paper: 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

NorOpenBookQA is intended to be used for zero- and few-shot evaluation of language models for Norwegian.

Dataset Creation

NorOpenBookQA is created by adapting the OpenBookQA 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 OpenBookQA using two strategies.

Manual translation and localization: The annotators manually translate the original examples, with localization that reflects Norwegian contexts where necessary.
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 67% of the examples (2377 out of 3515), 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.

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.
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': '1387-31',
    'question_stem': 'Hva er et eksempel på at flammer avgir lys?',
    'choices': {
        'label': ['A', 'B', 'C', 'D'],
        'text': [
            'En ovn er forhåndsvarmet og varsellampen lyser',
            'En fyrstikk tennes for å tenne en sigarett',
            'Et tent stearinlys i et vindu signaliserer til noen',
            'En brann som ble slukket for å sende røyksignaler'
        ], 
    },
    'answer': 'C',
    'fact': 'Flammer avgir lys',
    'curated': True
}

Nynorsk

{
    'id': '810-59',
    'question_stem': 'Konservering',
    'choices': {
        'label': ['A', 'B', 'C', 'D'],
        'text': [
            'fører til meir langvarig tørke av ressursar',
            'fører til meir langvarig tilgjenge av ressursar',
            'fører til større forbruk',
            'fører til mangel på ressursar'
        ],
    },
    'answer': 'B',
    'fact': 'Etter kvart som bruken av ein ressurs avtek, vil tida der ressursen er tilgjengeleg auke',
    'curated': False
}

Dataset Fields

id: an example id
question_stem: 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)
fact: a common knowledge core fact associated with the question (92% of the examples contain the fact)
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)