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Datasets:
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lama

Tasks:
Text Retrieval
Text Classification
Sub-tasks:
fact-checking-retrieval
text-scoring
Languages:
English
Size:
10K<n<100K
Tags:
probing
License:
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metadata
pretty_name: 'LAMA: LAnguage Model Analysis'
annotations_creators:
  - crowdsourced
  - expert-generated
  - machine-generated
language_creators:
  - crowdsourced
  - expert-generated
  - machine-generated
language:
  - en
license:
  - cc-by-4.0
multilinguality:
  - monolingual
size_categories:
  - 10K<n<100K
  - 1K<n<10K
  - 1M<n<10M
  - n<1K
source_datasets:
  - extended|conceptnet5
  - extended|squad
task_categories:
  - text-retrieval
  - text-classification
task_ids:
  - fact-checking-retrieval
  - text-scoring
paperswithcode_id: lama
tags:
  - probing
dataset_info:
  - config_name: trex
    features:
      - name: uuid
        dtype: string
      - name: obj_uri
        dtype: string
      - name: obj_label
        dtype: string
      - name: sub_uri
        dtype: string
      - name: sub_label
        dtype: string
      - name: predicate_id
        dtype: string
      - name: sub_surface
        dtype: string
      - name: obj_surface
        dtype: string
      - name: masked_sentence
        dtype: string
      - name: template
        dtype: string
      - name: template_negated
        dtype: string
      - name: label
        dtype: string
      - name: description
        dtype: string
      - name: type
        dtype: string
    splits:
      - name: train
        num_bytes: 656913189
        num_examples: 1304391
    download_size: 74652201
    dataset_size: 656913189
  - config_name: squad
    features:
      - name: id
        dtype: string
      - name: sub_label
        dtype: string
      - name: obj_label
        dtype: string
      - name: negated
        dtype: string
      - name: masked_sentence
        dtype: string
    splits:
      - name: train
        num_bytes: 57188
        num_examples: 305
    download_size: 74639115
    dataset_size: 57188
  - config_name: google_re
    features:
      - name: pred
        dtype: string
      - name: sub
        dtype: string
      - name: obj
        dtype: string
      - name: evidences
        dtype: string
      - name: judgments
        dtype: string
      - name: sub_w
        dtype: string
      - name: sub_label
        dtype: string
      - name: sub_aliases
        dtype: string
      - name: obj_w
        dtype: string
      - name: obj_label
        dtype: string
      - name: obj_aliases
        dtype: string
      - name: uuid
        dtype: string
      - name: masked_sentence
        dtype: string
      - name: template
        dtype: string
      - name: template_negated
        dtype: string
    splits:
      - name: train
        num_bytes: 7638657
        num_examples: 6106
    download_size: 74639115
    dataset_size: 7638657
  - config_name: conceptnet
    features:
      - name: uuid
        dtype: string
      - name: sub
        dtype: string
      - name: obj
        dtype: string
      - name: pred
        dtype: string
      - name: obj_label
        dtype: string
      - name: masked_sentence
        dtype: string
      - name: negated
        dtype: string
    splits:
      - name: train
        num_bytes: 4130000
        num_examples: 29774
    download_size: 74639115
    dataset_size: 4130000
config_names:
  - conceptnet
  - google_re
  - squad
  - trex

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

Table of Contents

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. The dataset include a subset of Google_RE (https://code.google.com/archive/p/relation-extraction-corpus/), TRex (subset of wikidata triples), Conceptnet (https://github.com/commonsense/conceptnet5/wiki) and Squad. There are configs for each of "google_re", "trex", "conceptnet" and "squad", respectively.

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 of the dataset includes "negated" sentences as well as the masked sentence. Also, certain of the config includes "template" and "template_negated" fields of the form "[X] some text [Y]", where [X] and [Y] are the subject and object slots respectively of certain relations.

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

Languages

en

Dataset Structure

Data Instances

The trex config has the following fields:

{'description': 'the item (an institution, law, public office ...) or statement belongs to or has power over or applies to the value (a territorial jurisdiction: a country, state, municipality, ...)', 'label': 'applies to jurisdiction', 'masked_sentence': 'It is known as a principality as it is a monarchy headed by two Co-Princes – the Spanish/Roman Catholic Bishop of Urgell and the President of [MASK].', 'obj_label': 'France', 'obj_surface': 'France', 'obj_uri': 'Q142', 'predicate_id': 'P1001', 'sub_label': 'president of the French Republic', 'sub_surface': 'President', 'sub_uri': 'Q191954', 'template': '[X] is a legal term in [Y] .', 'template_negated': '[X] is not a legal term in [Y] .', 'type': 'N-M', 'uuid': '3fe3d4da-9df9-45ba-8109-784ce5fba38a'}

The conceptnet config has the following fields:

{'masked_sentence': 'One of the things you do when you are alive is [MASK].', 'negated': '', 'obj': 'think', 'obj_label': 'think', 'pred': 'HasSubevent', 'sub': 'alive', 'uuid': 'd4f11631dde8a43beda613ec845ff7d1'}

The squad config has the following fields:

{'id': '56be4db0acb8001400a502f0_0', 'masked_sentence': 'To emphasize the 50th anniversary of the Super Bowl the [MASK] color was used.', 'negated': "['To emphasize the 50th anniversary of the Super Bowl the [MASK] color was not used.']", 'obj_label': 'gold', 'sub_label': 'Squad'}

The google_re config has the following fields:

{'evidences': '[{\'url\': \'http://en.wikipedia.org/wiki/Peter_F._Martin\', \'snippet\': "Peter F. Martin (born 1941) is an American politician who is a Democratic member of the Rhode Island House of Representatives. He has represented the 75th District Newport since 6 January 2009. He is currently serves on the House Committees on Judiciary, Municipal Government, and Veteran\'s Affairs. During his first term of office he served on the House Committees on Small Business and Separation of Powers & Government Oversight. In August 2010, Representative Martin was appointed as a Commissioner on the Atlantic States Marine Fisheries Commission", \'considered_sentences\': [\'Peter F Martin (born 1941) is an American politician who is a Democratic member of the Rhode Island House of Representatives .\']}]', 'judgments': "[{'rater': '18349444711114572460', 'judgment': 'yes'}, {'rater': '17595829233063766365', 'judgment': 'yes'}, {'rater': '4593294093459651288', 'judgment': 'yes'}, {'rater': '7387074196865291426', 'judgment': 'yes'}, {'rater': '17154471385681223613', 'judgment': 'yes'}]", 'masked_sentence': 'Peter F Martin (born [MASK]) is an American politician who is a Democratic member of the Rhode Island House of Representatives .', 'obj': '1941', 'obj_aliases': '[]', 'obj_label': '1941', 'obj_w': 'None', 'pred': '/people/person/date_of_birth', 'sub': '/m/09gb0bw', 'sub_aliases': '[]', 'sub_label': 'Peter F. Martin', 'sub_w': 'None', 'template': '[X] (born [Y]).', 'template_negated': '[X] (not born [Y]).', 'uuid': '18af2dac-21d3-4c42-aff5-c247f245e203'}

Data Fields

The trex config has the following fields:

  • uuid: the id
  • obj_uri: a uri for the object slot
  • obj_label: a label for the object slot
  • sub_uri: a uri for the subject slot
  • sub_label: a label for the subject slot
  • predicate_id: the predicate/relationship
  • sub_surface: the surface text for the subject
  • obj_surface: The surface text for the object. This is the word that should be predicted by the [MASK] token.
  • masked_sentence: The masked sentence used to probe, with the object word replaced with [MASK]
  • template: A pattern of text for extracting the relationship, object and subject of the form "[X] some text [Y]", where [X] and [Y] are the subject and object slots respectively. template may be missing and replaced with an empty string.
  • template_negated: Same as above, except the [Y] is not the object. template_negated may be missing and replaced with empty strings.
  • label: the label for the relationship/predicate. label may be missing and replaced with an empty string.
  • description': a description of the relationship/predicate. description may be missing and replaced with an empty string.
  • type: a type id for the relationship/predicate. type may be missing and replaced with an empty string.

The conceptnet config has the following fields:

  • uuid: the id
  • sub: the subject. subj may be missing and replaced with an empty string.
  • obj: the object to be predicted. obj may be missing and replaced with an empty string.
  • pred: the predicate/relationship
  • obj_label: the object label
  • masked_sentence: The masked sentence used to probe, with the object word replaced with [MASK]
  • negated: same as above, except [MASK] is replaced by something that is not the object word. negated may be missing and replaced with empty strings.

The squad config has the following fields:

  • id: the id
  • sub_label: the subject label
  • obj_label: the object label that is being predicted
  • masked_sentence: The masked sentence used to probe, with the object word replaced with [MASK]
  • negated: same as above, except [MASK] is replaced by something that is not the object word. negated may be missing and replaced with empty strings.

The google_re config has the following fields:

  • uuid: the id
  • pred: the predicate
  • sub: the subject. subj may be missing and replaced with an empty string.
  • obj: the object. obj may be missing and replaced with an empty string.
  • evidences: flattened json string that provides evidence for predicate. parse this json string to get more 'snippet' information.
  • judgments: data about judgments
  • sub_q: unknown
  • sub_label: label for the subject
  • sub_aliases: unknown
  • obj_w: unknown
  • obj_label: label for the object
  • obj_aliases: unknown
  • masked_sentence: The masked sentence used to probe, with the object word replaced with [MASK]
  • template: A pattern of text for extracting the relationship, object and subject of the form "[X] some text [Y]", where [X] and [Y] are the subject and object slots respectively.
  • template_negated: Same as above, except the [Y] is not the object.

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} }

@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} }

Contributions

Thanks to @ontocord for adding this dataset.