Datasets:
sentence_good
stringlengths 40
81
| sentence_bad
stringlengths 40
81
| field
stringclasses 1
value | linguistics_term
stringclasses 1
value | UID
stringclasses 1
value | simple_LM_method
bool 1
class | one_prefix_method
bool 1
class | two_prefix_method
bool 1
class | lexically_identical
bool 1
class | pair_id
int32 0
999
|
---|---|---|---|---|---|---|---|---|---|
Who should Derek hug after shocking Richard? | Who should Derek hug Richard after shocking? | syntax | island_effects | adjunct_island | true | false | false | true | 0 |
What had Theresa walked through while talking about that high school? | What had Theresa walked through that high school while talking about? | syntax | island_effects | adjunct_island | true | false | false | true | 1 |
Who will Katherine discover without hiring Erin? | Who will Katherine discover Erin without hiring? | syntax | island_effects | adjunct_island | true | false | false | true | 2 |
Who has Colleen aggravated before kissing Judy? | Who has Colleen aggravated Judy before kissing? | syntax | island_effects | adjunct_island | true | false | false | true | 3 |
What could a lot of cats break while finding all convertibles? | What could a lot of cats break all convertibles while finding? | syntax | island_effects | adjunct_island | true | false | false | true | 4 |
Who have most people discovered while embarrassing Erin? | Who have most people discovered Erin while embarrassing? | syntax | island_effects | adjunct_island | true | false | false | true | 5 |
Who was William firing before talking about Maria? | Who was William firing Maria before talking about? | syntax | island_effects | adjunct_island | true | false | false | true | 6 |
What is Denise descending while hiding a lot of hills? | What is Denise descending a lot of hills while hiding? | syntax | island_effects | adjunct_island | true | false | false | true | 7 |
Who does John leave while alarming Beverly? | Who does John leave Beverly while alarming? | syntax | island_effects | adjunct_island | true | false | false | true | 8 |
What was Melanie going to after taking those rivers? | What was Melanie going to those rivers after taking? | syntax | island_effects | adjunct_island | true | false | false | true | 9 |
Who could Bethany research before discussing Winston Churchill? | Who could Bethany research Winston Churchill before discussing? | syntax | island_effects | adjunct_island | true | false | false | true | 10 |
What could Jessica sell before noticing these spotlights? | What could Jessica sell these spotlights before noticing? | syntax | island_effects | adjunct_island | true | false | false | true | 11 |
What had Helen biked to without bothering Spain? | What had Helen biked to Spain without bothering? | syntax | island_effects | adjunct_island | true | false | false | true | 12 |
Who is Mary irritating after approaching Kenneth? | Who is Mary irritating Kenneth after approaching? | syntax | island_effects | adjunct_island | true | false | false | true | 13 |
Who might Rose flee from before returning to this customer? | Who might Rose flee from this customer before returning to? | syntax | island_effects | adjunct_island | true | false | false | true | 14 |
What will Janice research after boasting about politics? | What will Janice research politics after boasting about? | syntax | island_effects | adjunct_island | true | false | false | true | 15 |
Who had Karla aggravated without finding Donald? | Who had Karla aggravated Donald without finding? | syntax | island_effects | adjunct_island | true | false | false | true | 16 |
Who should a government reference before shocking Jesus? | Who should a government reference Jesus before shocking? | syntax | island_effects | adjunct_island | true | false | false | true | 17 |
What had Aaron sounded like while cleaning the museum? | What had Aaron sounded like the museum while cleaning? | syntax | island_effects | adjunct_island | true | false | false | true | 18 |
What is Brenda arriving at while exiting many schools? | What is Brenda arriving at many schools while exiting? | syntax | island_effects | adjunct_island | true | false | false | true | 19 |
Who is a window hurting without disturbing Sabrina? | Who is a window hurting Sabrina without disturbing? | syntax | island_effects | adjunct_island | true | false | false | true | 20 |
Who had Jesus bothered after leaving Lori? | Who had Jesus bothered Lori after leaving? | syntax | island_effects | adjunct_island | true | false | false | true | 21 |
Who are a lot of hamsters disgusting while finding Ronald? | Who are a lot of hamsters disgusting Ronald while finding? | syntax | island_effects | adjunct_island | true | false | false | true | 22 |
Who could Amelia leave while appreciating Paul? | Who could Amelia leave Paul while appreciating? | syntax | island_effects | adjunct_island | true | false | false | true | 23 |
What have many ladies dropped by without talking about all malls? | What have many ladies dropped by all malls without talking about? | syntax | island_effects | adjunct_island | true | false | false | true | 24 |
What is Larry dropping by after lifting a grocery store? | What is Larry dropping by a grocery store after lifting? | syntax | island_effects | adjunct_island | true | false | false | true | 25 |
Who is that student hugging before stunning Catherine? | Who is that student hugging Catherine before stunning? | syntax | island_effects | adjunct_island | true | false | false | true | 26 |
Who were those schools appreciating without scaring Alice? | Who were those schools appreciating Alice without scaring? | syntax | island_effects | adjunct_island | true | false | false | true | 27 |
What did Dan sell after revealing some plane? | What did Dan sell some plane after revealing? | syntax | island_effects | adjunct_island | true | false | false | true | 28 |
What had Marcus climbed down before buying many mountains? | What had Marcus climbed down many mountains before buying? | syntax | island_effects | adjunct_island | true | false | false | true | 29 |
What has Jacqueline scanned before revealing that report? | What has Jacqueline scanned that report before revealing? | syntax | island_effects | adjunct_island | true | false | false | true | 30 |
Who is Tanya returning to while astounding Donald? | Who is Tanya returning to Donald while astounding? | syntax | island_effects | adjunct_island | true | false | false | true | 31 |
Who had Kirsten referenced without boring Liam? | Who had Kirsten referenced Liam without boring? | syntax | island_effects | adjunct_island | true | false | false | true | 32 |
What are some drivers touring after going to every hill? | What are some drivers touring every hill after going to? | syntax | island_effects | adjunct_island | true | false | false | true | 33 |
Who would Mark visit while kissing Marla? | Who would Mark visit Marla while kissing? | syntax | island_effects | adjunct_island | true | false | false | true | 34 |
What is Scott cleaning after cleaning a pie? | What is Scott cleaning a pie after cleaning? | syntax | island_effects | adjunct_island | true | false | false | true | 35 |
Who has some doctor listened to after firing that actress? | Who has some doctor listened to that actress after firing? | syntax | island_effects | adjunct_island | true | false | false | true | 36 |
Who has that lady approached after alarming Ellen? | Who has that lady approached Ellen after alarming? | syntax | island_effects | adjunct_island | true | false | false | true | 37 |
What can Randolf bike to before discussing the lakes? | What can Randolf bike to the lakes before discussing? | syntax | island_effects | adjunct_island | true | false | false | true | 38 |
What had some senator toured before hiding those cafes? | What had some senator toured those cafes before hiding? | syntax | island_effects | adjunct_island | true | false | false | true | 39 |
Who was Eric impressing without admiring Christina? | Who was Eric impressing Christina without admiring? | syntax | island_effects | adjunct_island | true | false | false | true | 40 |
What do most drivers climb up without noticing most hills? | What do most drivers climb up most hills without noticing? | syntax | island_effects | adjunct_island | true | false | false | true | 41 |
Who had Gregory worked with while noticing Julie? | Who had Gregory worked with Julie while noticing? | syntax | island_effects | adjunct_island | true | false | false | true | 42 |
Who was Rose astounding while hiding many waiters? | Who was Rose astounding many waiters while hiding? | syntax | island_effects | adjunct_island | true | false | false | true | 43 |
What is Karen researching before walking through that mountain? | What is Karen researching that mountain before walking through? | syntax | island_effects | adjunct_island | true | false | false | true | 44 |
Who could Dana boast about after disturbing Brenda? | Who could Dana boast about Brenda after disturbing? | syntax | island_effects | adjunct_island | true | false | false | true | 45 |
What was this bird aggravating before annoying every hospital? | What was this bird aggravating every hospital before annoying? | syntax | island_effects | adjunct_island | true | false | false | true | 46 |
Who had Colleen known after concealing Stephen? | Who had Colleen known Stephen after concealing? | syntax | island_effects | adjunct_island | true | false | false | true | 47 |
What are these customers arriving at without alarming Spain? | What are these customers arriving at Spain without alarming? | syntax | island_effects | adjunct_island | true | false | false | true | 48 |
What has Angela admired while cleaning shoes? | What has Angela admired shoes while cleaning? | syntax | island_effects | adjunct_island | true | false | false | true | 49 |
What is Becky selling before noticing some hospital? | What is Becky selling some hospital before noticing? | syntax | island_effects | adjunct_island | true | false | false | true | 50 |
Who had many cashiers concealed after investigating Nina? | Who had many cashiers concealed Nina after investigating? | syntax | island_effects | adjunct_island | true | false | false | true | 51 |
Who had Melanie admired before listening to Chad? | Who had Melanie admired Chad before listening to? | syntax | island_effects | adjunct_island | true | false | false | true | 52 |
Who does Bethany visit before scaring Amanda? | Who does Bethany visit Amanda before scaring? | syntax | island_effects | adjunct_island | true | false | false | true | 53 |
What is Tiffany exiting before climbing down the mountain? | What is Tiffany exiting the mountain before climbing down? | syntax | island_effects | adjunct_island | true | false | false | true | 54 |
Who had Susan insulted without boring Heather? | Who had Susan insulted Heather without boring? | syntax | island_effects | adjunct_island | true | false | false | true | 55 |
Who has Curtis noticed while investigating Steve? | Who has Curtis noticed Steve while investigating? | syntax | island_effects | adjunct_island | true | false | false | true | 56 |
Who has Kayla disliked after disgusting Derek? | Who has Kayla disliked Derek after disgusting? | syntax | island_effects | adjunct_island | true | false | false | true | 57 |
What is Martin skated around without buying these hospitals? | What is Martin skated around these hospitals without buying? | syntax | island_effects | adjunct_island | true | false | false | true | 58 |
What was Beth dropping by after bringing all glaciers? | What was Beth dropping by all glaciers after bringing? | syntax | island_effects | adjunct_island | true | false | false | true | 59 |
Who had Rhonda distracted while finding Chad? | Who had Rhonda distracted Chad while finding? | syntax | island_effects | adjunct_island | true | false | false | true | 60 |
What has Bethany driven to before biking to a lot of hills? | What has Bethany driven to a lot of hills before biking to? | syntax | island_effects | adjunct_island | true | false | false | true | 61 |
Who have the slopes hurt before disgusting a lot of adults? | Who have the slopes hurt a lot of adults before disgusting? | syntax | island_effects | adjunct_island | true | false | false | true | 62 |
What will Brad wear before noticing these scarves? | What will Brad wear these scarves before noticing? | syntax | island_effects | adjunct_island | true | false | false | true | 63 |
What had Steven boycotted before running around the art gallery? | What had Steven boycotted the art gallery before running around? | syntax | island_effects | adjunct_island | true | false | false | true | 64 |
Who do the Borgias like before aggravating Mark? | Who do the Borgias like Mark before aggravating? | syntax | island_effects | adjunct_island | true | false | false | true | 65 |
What had the patients resembled without hiding most sketches? | What had the patients resembled most sketches without hiding? | syntax | island_effects | adjunct_island | true | false | false | true | 66 |
Who would Jesus hug without discovering Bradley? | Who would Jesus hug Bradley without discovering? | syntax | island_effects | adjunct_island | true | false | false | true | 67 |
Who has Mark sounded like before finding that doctor? | Who has Mark sounded like that doctor before finding? | syntax | island_effects | adjunct_island | true | false | false | true | 68 |
Who has Rebecca fled from without listening to Kirsten? | Who has Rebecca fled from Kirsten without listening to? | syntax | island_effects | adjunct_island | true | false | false | true | 69 |
Who was Ellen escaping from before curing Julia? | Who was Ellen escaping from Julia before curing? | syntax | island_effects | adjunct_island | true | false | false | true | 70 |
What does Travis fix after breaking every carriage? | What does Travis fix every carriage after breaking? | syntax | island_effects | adjunct_island | true | false | false | true | 71 |
What might Tanya walk through after hiding every cafe? | What might Tanya walk through every cafe after hiding? | syntax | island_effects | adjunct_island | true | false | false | true | 72 |
What has Ella brought after finding all icicles? | What has Ella brought all icicles after finding? | syntax | island_effects | adjunct_island | true | false | false | true | 73 |
What was Valerie selling after climbing down some slope? | What was Valerie selling some slope after climbing down? | syntax | island_effects | adjunct_island | true | false | false | true | 74 |
What could Douglas bike to without admiring every mountain? | What could Douglas bike to every mountain without admiring? | syntax | island_effects | adjunct_island | true | false | false | true | 75 |
What would those teenagers research before boycotting some malls? | What would those teenagers research some malls before boycotting? | syntax | island_effects | adjunct_island | true | false | false | true | 76 |
What can Dan clean before cleaning every screen? | What can Dan clean every screen before cleaning? | syntax | island_effects | adjunct_island | true | false | false | true | 77 |
Who would Deborah see while hugging Christina? | Who would Deborah see Christina while hugging? | syntax | island_effects | adjunct_island | true | false | false | true | 78 |
Who can Gregory talk about before hugging Emily? | Who can Gregory talk about Emily before hugging? | syntax | island_effects | adjunct_island | true | false | false | true | 79 |
What should Elaine argue about before admiring books? | What should Elaine argue about books before admiring? | syntax | island_effects | adjunct_island | true | false | false | true | 80 |
What had Jason dropped by before selling those schools? | What had Jason dropped by those schools before selling? | syntax | island_effects | adjunct_island | true | false | false | true | 81 |
What will the guys notice before shocking a lot of museums? | What will the guys notice a lot of museums before shocking? | syntax | island_effects | adjunct_island | true | false | false | true | 82 |
Who could Monet worry while thinking about Dana? | Who could Monet worry Dana while thinking about? | syntax | island_effects | adjunct_island | true | false | false | true | 83 |
Who could Thomas observe without distracting Peter? | Who could Thomas observe Peter without distracting? | syntax | island_effects | adjunct_island | true | false | false | true | 84 |
Who has Suzanne admired after scaring Connie? | Who has Suzanne admired Connie after scaring? | syntax | island_effects | adjunct_island | true | false | false | true | 85 |
Who can some closets disgust without shocking that dancer? | Who can some closets disgust that dancer without shocking? | syntax | island_effects | adjunct_island | true | false | false | true | 86 |
Who has Bruce annoyed while investigating Kevin? | Who has Bruce annoyed Kevin while investigating? | syntax | island_effects | adjunct_island | true | false | false | true | 87 |
What was Bethany lifting without returning to these cafes? | What was Bethany lifting these cafes without returning to? | syntax | island_effects | adjunct_island | true | false | false | true | 88 |
What should Wendy boycott after questioning every hospital? | What should Wendy boycott every hospital after questioning? | syntax | island_effects | adjunct_island | true | false | false | true | 89 |
What are these pedestrians selling after finding that bird? | What are these pedestrians selling that bird after finding? | syntax | island_effects | adjunct_island | true | false | false | true | 90 |
What is Laurie buying before cleaning the car? | What is Laurie buying the car before cleaning? | syntax | island_effects | adjunct_island | true | false | false | true | 91 |
What does Matt embarrass before bothering every legislature? | What does Matt embarrass every legislature before bothering? | syntax | island_effects | adjunct_island | true | false | false | true | 92 |
What have some drivers worn without finding a shawl? | What have some drivers worn a shawl without finding? | syntax | island_effects | adjunct_island | true | false | false | true | 93 |
Who can Kimberley help before healing Martin? | Who can Kimberley help Martin before healing? | syntax | island_effects | adjunct_island | true | false | false | true | 94 |
Who have most students distracted before impressing Marla? | Who have most students distracted Marla before impressing? | syntax | island_effects | adjunct_island | true | false | false | true | 95 |
What was Dawn boycotting after describing public parks? | What was Dawn boycotting public parks after describing? | syntax | island_effects | adjunct_island | true | false | false | true | 96 |
Who was Cynthia observing while thinking about Rose? | Who was Cynthia observing Rose while thinking about? | syntax | island_effects | adjunct_island | true | false | false | true | 97 |
What do many fish break before breaking the truck? | What do many fish break the truck before breaking? | syntax | island_effects | adjunct_island | true | false | false | true | 98 |
Who was Ann thinking about without distracting some cashiers? | Who was Ann thinking about some cashiers without distracting? | syntax | island_effects | adjunct_island | true | false | false | true | 99 |
Dataset Card for "blimp"
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. Source: https://github.com/alexwarstadt/blimp#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. }
}
Errata
Some results were misreported in the published TACL version. Please refer to the corrected version on arXiv: https://arxiv.org/abs/1912.00582
Contributions
Thanks to @lhoestq, @patrickvonplaten, @thomwolf for adding this dataset.
- Downloads last month
- 8,326