metadata

annotations_creators:
  - machine-generated
language_creators:
  - machine-generated
language:
  - en
license:
  - cc-by-4.0
multilinguality:
  - monolingual
size_categories:
  trex:
    - 1M<n<10M
task_categories:
  - text-retrieval
  - text-classification
task_ids:
  - fact-checking-retrieval
  - text-scoring
paperswithcode_id: lama
pretty_name: 'LAMA: LAnguage Model Analysis - BigScience version'
tags:
  - probing

Dataset Card for LAMA: LAnguage Model Analysis - a dataset for probing and analyzing the factual and commonsense knowledge contained in pretrained language models.

Dataset Description
Dataset Structure
Dataset Creation
Considerations for Using the Data
Additional Information

Dataset Description

Homepage: https://github.com/facebookresearch/LAMA
Repository: https://github.com/facebookresearch/LAMA
Paper: @inproceedings{petroni2019language, title={Language Models as Knowledge Bases?}, author={F. Petroni, T. Rockt{"{a}}schel, A. H. Miller, P. Lewis, A. Bakhtin, Y. Wu and S. Riedel}, booktitle={In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019}, year={2019} }

@inproceedings{petroni2020how, title={How Context Affects Language Models' Factual Predictions}, author={Fabio Petroni and Patrick Lewis and Aleksandra Piktus and Tim Rockt{"a}schel and Yuxiang Wu and Alexander H. Miller and Sebastian Riedel}, booktitle={Automated Knowledge Base Construction}, year={2020}, url={https://openreview.net/forum?id=025X0zPfn} }

Dataset Summary

This dataset provides the data for LAMA. This dataset only contains TRex (subset of wikidata triples).

The dataset includes some cleanup, and addition of a masked sentence and associated answers for the [MASK] token. The accuracy in predicting the [MASK] token shows how well the language model knows facts and common sense information. The [MASK] tokens are only for the "object" slots.

This version also contains questions instead of templates that can be used to probe also non-masking models.

See the paper for more details. For more information, also see: https://github.com/facebookresearch/LAMA

Languages

Dataset Structure

Data Instances

The trex config has the following fields:

{'uuid': 'a37257ae-4cbb-4309-a78a-623036c96797', 'sub_label': 'Pianos Become the Teeth', 'predicate_id': 'P740', 'obj_label': 'Baltimore', 'template': '[X] was founded in [Y] .', 'type': 'N-1', 'question': 'Where was [X] founded?'} 34039

Data Splits

There are no data splits.

Dataset Creation

Curation Rationale

This dataset was gathered and created to probe what language models understand.

Source Data

Initial Data Collection and Normalization

See the reaserch paper and website for more detail. The dataset was created gathered from various other datasets with cleanups for probing.

Who are the source language producers?

The LAMA authors and the original authors of the various configs.

Annotations

Annotation process

Human annotations under the original datasets (conceptnet), and various machine annotations.

Who are the annotators?

Human annotations and machine annotations.

Personal and Sensitive Information

Unkown, but likely names of famous people.

Considerations for Using the Data

Social Impact of Dataset

The goal for the work is to probe the understanding of language models.

Discussion of Biases

Since the data is from human annotators, there is likely to be baises.

[More Information Needed]

Other Known Limitations

The original documentation for the datafields are limited.

Additional Information

Dataset Curators

The authors of LAMA at Facebook and the authors of the original datasets.

Licensing Information

The Creative Commons Attribution-Noncommercial 4.0 International License. see https://github.com/facebookresearch/LAMA/blob/master/LICENSE

Citation Information

@inproceedings{petroni2019language, title={Language Models as Knowledge Bases?}, author={F. Petroni, T. Rockt{"{a}}schel, A. H. Miller, P. Lewis, A. Bakhtin, Y. Wu and S. Riedel}, booktitle={In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019}, year={2019} }

Datasets:

janck
/

bigscience-lama