Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
acceptability-classification
Languages:
English
Size:
10K - 100K
ArXiv:
License:
metadata
annotations_creators:
- crowdsourced
language_creators:
- machine-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- acceptability-classification
paperswithcode_id: blimp
pretty_name: BLiMP
dataset_info:
- config_name: adjunct_island
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- config_name: anaphor_gender_agreement
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- config_name: anaphor_number_agreement
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- config_name: regular_plural_subject_verb_agreement_1
features:
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dtype: string
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- config_name: sentential_subject_island
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dtype: string
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dtype: int32
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- config_name: superlative_quantifiers_1
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dtype: string
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- name: field
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- name: pair_id
dtype: int32
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- config_name: superlative_quantifiers_2
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- name: pair_id
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num_examples: 1000
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- config_name: tough_vs_raising_1
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dtype: bool
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dtype: int32
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- config_name: tough_vs_raising_2
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num_examples: 1000
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- config_name: wh_island
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- config_name: wh_questions_subject_gap
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- config_name: wh_vs_that_no_gap
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- config_name: wh_vs_that_no_gap_long_distance
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- name: pair_id
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- config_name: wh_vs_that_with_gap
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dtype: string
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dtype: string
- name: field
dtype: string
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dtype: bool
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dtype: int32
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dataset_size: 174781
- config_name: wh_vs_that_with_gap_long_distance
features:
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dtype: string
- name: sentence_bad
dtype: string
- name: field
dtype: string
- name: linguistics_term
dtype: string
- name: UID
dtype: string
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dtype: bool
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dtype: bool
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dtype: bool
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dtype: bool
- name: pair_id
dtype: int32
splits:
- name: train
num_bytes: 232990
num_examples: 1000
download_size: 425985
dataset_size: 232990
configs:
- config_name: adjunct_island
data_files:
- split: train
path: adjunct_island/train-*
- config_name: anaphor_gender_agreement
data_files:
- split: train
path: anaphor_gender_agreement/train-*
- config_name: anaphor_number_agreement
data_files:
- split: train
path: anaphor_number_agreement/train-*
- config_name: animate_subject_passive
data_files:
- split: train
path: animate_subject_passive/train-*
- config_name: animate_subject_trans
data_files:
- split: train
path: animate_subject_trans/train-*
- config_name: causative
data_files:
- split: train
path: causative/train-*
- config_name: complex_NP_island
data_files:
- split: train
path: complex_NP_island/train-*
Dataset Card for "blimp"
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage:
- Repository: https://github.com/alexwarstadt/blimp
- Paper: BLiMP: The Benchmark of Linguistic Minimal Pairs for English
- Point of Contact: More Information Needed
- Size of downloaded dataset files: 29.58 MB
- Size of the generated dataset: 11.45 MB
- Total amount of disk used: 41.03 MB
Dataset Summary
BLiMP is a challenge set for evaluating what language models (LMs) know about major grammatical phenomena in English. BLiMP consists of 67 sub-datasets, each containing 1000 minimal pairs isolating specific contrasts in syntax, morphology, or semantics. The data is automatically generated according to expert-crafted grammars.
Supported Tasks and Leaderboards
Languages
Dataset Structure
Data Instances
adjunct_island
- Size of downloaded dataset files: 0.36 MB
- Size of the generated dataset: 0.17 MB
- Total amount of disk used: 0.52 MB
An example of 'train' looks as follows.
{
"UID": "tough_vs_raising_1",
"field": "syntax_semantics",
"lexically_identical": false,
"linguistics_term": "control_raising",
"one_prefix_method": false,
"pair_id": 2,
"sentence_bad": "Benjamin's tutor was certain to boast about.",
"sentence_good": "Benjamin's tutor was easy to boast about.",
"simple_LM_method": true,
"two_prefix_method": false
}
anaphor_gender_agreement
- Size of downloaded dataset files: 0.44 MB
- Size of the generated dataset: 0.14 MB
- Total amount of disk used: 0.57 MB
An example of 'train' looks as follows.
{
"UID": "tough_vs_raising_1",
"field": "syntax_semantics",
"lexically_identical": false,
"linguistics_term": "control_raising",
"one_prefix_method": false,
"pair_id": 2,
"sentence_bad": "Benjamin's tutor was certain to boast about.",
"sentence_good": "Benjamin's tutor was easy to boast about.",
"simple_LM_method": true,
"two_prefix_method": false
}
anaphor_number_agreement
- Size of downloaded dataset files: 0.45 MB
- Size of the generated dataset: 0.14 MB
- Total amount of disk used: 0.59 MB
An example of 'train' looks as follows.
{
"UID": "tough_vs_raising_1",
"field": "syntax_semantics",
"lexically_identical": false,
"linguistics_term": "control_raising",
"one_prefix_method": false,
"pair_id": 2,
"sentence_bad": "Benjamin's tutor was certain to boast about.",
"sentence_good": "Benjamin's tutor was easy to boast about.",
"simple_LM_method": true,
"two_prefix_method": false
}
animate_subject_passive
- Size of downloaded dataset files: 0.46 MB
- Size of the generated dataset: 0.15 MB
- Total amount of disk used: 0.61 MB
An example of 'train' looks as follows.
{
"UID": "tough_vs_raising_1",
"field": "syntax_semantics",
"lexically_identical": false,
"linguistics_term": "control_raising",
"one_prefix_method": false,
"pair_id": 2,
"sentence_bad": "Benjamin's tutor was certain to boast about.",
"sentence_good": "Benjamin's tutor was easy to boast about.",
"simple_LM_method": true,
"two_prefix_method": false
}
animate_subject_trans
- Size of downloaded dataset files: 0.43 MB
- Size of the generated dataset: 0.13 MB
- Total amount of disk used: 0.57 MB
An example of 'train' looks as follows.
{
"UID": "tough_vs_raising_1",
"field": "syntax_semantics",
"lexically_identical": false,
"linguistics_term": "control_raising",
"one_prefix_method": false,
"pair_id": 2,
"sentence_bad": "Benjamin's tutor was certain to boast about.",
"sentence_good": "Benjamin's tutor was easy to boast about.",
"simple_LM_method": true,
"two_prefix_method": false
}
Data Fields
The data fields are the same among all splits.
adjunct_island
sentence_good
: astring
feature.sentence_bad
: astring
feature.field
: astring
feature.linguistics_term
: astring
feature.UID
: astring
feature.simple_LM_method
: abool
feature.one_prefix_method
: abool
feature.two_prefix_method
: abool
feature.lexically_identical
: abool
feature.pair_id
: aint32
feature.
anaphor_gender_agreement
sentence_good
: astring
feature.sentence_bad
: astring
feature.field
: astring
feature.linguistics_term
: astring
feature.UID
: astring
feature.simple_LM_method
: abool
feature.one_prefix_method
: abool
feature.two_prefix_method
: abool
feature.lexically_identical
: abool
feature.pair_id
: aint32
feature.
anaphor_number_agreement
sentence_good
: astring
feature.sentence_bad
: astring
feature.field
: astring
feature.linguistics_term
: astring
feature.UID
: astring
feature.simple_LM_method
: abool
feature.one_prefix_method
: abool
feature.two_prefix_method
: abool
feature.lexically_identical
: abool
feature.pair_id
: aint32
feature.
animate_subject_passive
sentence_good
: astring
feature.sentence_bad
: astring
feature.field
: astring
feature.linguistics_term
: astring
feature.UID
: astring
feature.simple_LM_method
: abool
feature.one_prefix_method
: abool
feature.two_prefix_method
: abool
feature.lexically_identical
: abool
feature.pair_id
: aint32
feature.
animate_subject_trans
sentence_good
: astring
feature.sentence_bad
: astring
feature.field
: astring
feature.linguistics_term
: astring
feature.UID
: astring
feature.simple_LM_method
: abool
feature.one_prefix_method
: abool
feature.two_prefix_method
: abool
feature.lexically_identical
: abool
feature.pair_id
: aint32
feature.
Data Splits
name | train |
---|---|
adjunct_island | 1000 |
anaphor_gender_agreement | 1000 |
anaphor_number_agreement | 1000 |
animate_subject_passive | 1000 |
animate_subject_trans | 1000 |
Dataset Creation
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Licensing Information
BLiMP is distributed under a CC-BY license.
Citation Information
@article{warstadt2020blimp,
author = {Warstadt, Alex and Parrish, Alicia and Liu, Haokun and Mohananey, Anhad and Peng, Wei and Wang, Sheng-Fu and Bowman, Samuel R.},
title = {BLiMP: The Benchmark of Linguistic Minimal Pairs for English},
journal = {Transactions of the Association for Computational Linguistics},
volume = {8},
number = {},
pages = {377-392},
year = {2020},
doi = {10.1162/tacl\_a\_00321},
URL = {https://doi.org/10.1162/tacl_a_00321},
eprint = {https://doi.org/10.1162/tacl_a_00321},
abstract = { We introduce The Benchmark of Linguistic Minimal Pairs (BLiMP),1 a challenge set for evaluating the linguistic knowledge of language models (LMs) on major grammatical phenomena in English. BLiMP consists of 67 individual datasets, each containing 1,000 minimal pairs—that is, pairs of minimally different sentences that contrast in grammatical acceptability and isolate specific phenomenon in syntax, morphology, or semantics. We generate the data according to linguist-crafted grammar templates, and human aggregate agreement with the labels is 96.4\%. We evaluate n-gram, LSTM, and Transformer (GPT-2 and Transformer-XL) LMs by observing whether they assign a higher probability to the acceptable sentence in each minimal pair. We find that state-of-the-art models identify morphological contrasts related to agreement reliably, but they struggle with some subtle semantic and syntactic phenomena, such as negative polarity items and extraction islands. }
}
Contributions
Thanks to @lhoestq, @patrickvonplaten, @thomwolf for adding this dataset.