Datasets:

lm-pub-quiz
/

BEAR

license: cc-by-sa-4.0
dataset_info:
  - config_name: BEAR
    features:
      - name: composite_id
        dtype: string
      - name: relation
        dtype: string
      - name: item
        dtype: int64
      - name: template_index
        dtype: int64
      - name: template
        dtype: string
      - name: subject
        dtype: string
      - name: answer_options
        sequence: string
      - name: correct
        dtype: int64
      - name: text_options
        sequence: string
    splits:
      - name: test
        num_bytes: 46574625
        num_examples: 23193
    download_size: 6275852
    dataset_size: 46574625
  - config_name: BEAR_big
    features:
      - name: composite_id
        dtype: string
      - name: relation
        dtype: string
      - name: item
        dtype: int64
      - name: template_index
        dtype: int64
      - name: template
        dtype: string
      - name: subject
        dtype: string
      - name: answer_options
        sequence: string
      - name: correct
        dtype: int64
      - name: text_options
        sequence: string
    splits:
      - name: test
        num_bytes: 622640960
        num_examples: 122748
    download_size: 77671769
    dataset_size: 622640960
configs:
  - config_name: BEAR
    data_files:
      - split: test
        path: BEAR/test-*
    default: true
  - config_name: BEAR_big
    data_files:
      - split: test
        path: BEAR_big/test-*

BEAR Dataset

Dataset to evaluate common factual knowledge in language models. This dataset was created as part of the paper "BEAR: A Unified Framework for Evaluating Relational Knowledge in Causal and Masked Language Models".

For more information visit the LM Pub Quiz website.

Dataset Structure

Data Instances

For each instance, there is a template string, a subject to be filled in, and a list of object strings (one of which is correct), as well as a template used to create the specific multiple choice item.

Data Fields

composite_id: A unique string (each part of the dataset) identifying the item within the dataset. The string is made up from relation, item, and template_index.
relation: An string identifiying the wikidata relation (between the subject and the answer options).
item: The indext of the item within the relation.
template_index: The index of the template used for this instance.
template: The template used to create the text_options.
subject: The subject of the relation.
answer_options: Possible objects in the relation.
correct: The index of the correct object (index of the answer in answer_options / correct statement in text_options).
text_options: Alternative textual statements one of which is correct (identified by the index in correct).

Citation

When using the dataset or library, please cite the following paper:

@inproceedings{wiland-etal-2024-bear,
    title = "{BEAR}: A Unified Framework for Evaluating Relational Knowledge in Causal and Masked Language Models",
    author = "Wiland, Jacek  and
      Ploner, Max  and
      Akbik, Alan",
    editor = "Duh, Kevin  and
      Gomez, Helena  and
      Bethard, Steven",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2024",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.findings-naacl.155/",
    doi = "10.18653/v1/2024.findings-naacl.155",
    pages = "2393--2411"
}