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Linguistic Phenomena Challenge Data
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About
This dataset, known as BLiMP (Grammatical Phenomena Evaluation), serves as a challenge set for assessing what language models understand about significant grammatical structures in English. It consists of 67 distinct sub-datasets, each carefully designed to isolate specific contrasts in syntax, morphology, or semantics. The data is generated automatically using expert-crafted grammars. It can be valuable for training machine learning models to automatically identify whether a sentence is grammatically correct or incorrect.
Columns
The dataset typically includes the following columns:
- sentence_good: A sentence that is grammatically correct. (string)
- sentence_bad: A sentence that is grammatically incorrect. (string)
- field: The academic or research field from which the sentence's context is derived. (string)
- linguistics_term: The specific linguistics term associated with the grammatical phenomenon illustrated by the sentence. (string)
- simple_LM_method: Describes the simple language model method employed to generate the sentence. (string)
- one_prefix_method: Details the one-prefix method used during sentence generation. (string)
- two_prefix_method: Describes the two-prefix method used for sentence generation. (string)
- UID: A unique identifier for each entry. (string)
- lexically_identical: Indicates whether the minimal pair is lexically identical. (Boolean)
- pair_id: An identifier for the minimal pair. (Integer)
Distribution
The dataset is structured into 67 sub-datasets. Each of these sub-datasets contains 1000 minimal pairs, offering a focused evaluation of grammatical contrasts. Data files are typically provided in CSV format, with examples such as
wh_questions_subject_gap_long_distance_train.csv. The total number of rows or records across all sub-datasets is not explicitly stated, but each sub-dataset consistently contains 1000 pairs.Usage
This dataset is ideally suited for:
- Evaluating Language Models: Assessing how well language models comprehend and process major grammatical phenomena.
- Machine Learning Model Training: Training models to discern grammatically correct sentences from incorrect ones.
- Linguistic Research: Investigating specific contrasts in English syntax, morphology, and semantics.
- Developing Grammar Correction Tools: Building and improving automated grammar checking systems.
Coverage
The dataset focuses on major grammatical phenomena exclusively in the English language. Its regional coverage is global, making it relevant for a wide range of applications. No specific time range or demographic scope beyond the English language itself is detailed in the available information.
License
CC0
Who Can Use It
This dataset is beneficial for a variety of users, including:
- Natural Language Processing (NLP) Researchers: For advancing the understanding and capabilities of language models.
- Machine Learning Engineers: For developing and refining models capable of grammar detection and correction.
- Linguists: For in-depth analysis of grammatical structures and their variations.
- Academics and Students: For educational purposes and research projects in computational linguistics and AI.
- Developers: For integrating grammar evaluation or correction features into applications.
Dataset Name Suggestions
- BLiMP English Grammar Evaluation Set
- Linguistic Phenomena Challenge Data
- Grammar Correctness Dataset for LMs
- English Syntactic & Morphological Pairs
- Automated Grammar Assessment Data
Attributes
Original Data Source: BLiMP (Grammatical Phenomena Evaluation)
QUALITY