| """ |
| BLiMP: A Benchmark of Linguistic Minimal Pairs for English |
| https://arxiv.org/abs/1912.00582 |
| |
| 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. |
| |
| Homepage: https://github.com/alexwarstadt/blimp |
| """ |
| from lm_eval.api.task import PromptSourceTask |
| from typing import Optional, List |
| from datasets import load_dataset |
|
|
|
|
| _CITATION = """ |
| @article{warstadt2019blimp, |
| 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/tacla00321}, |
| 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. } |
| } |
| """ |
|
|
| class CompsTask(PromptSourceTask): |
| DATASET_PATH = "kanishka/comps" |
|
|
| def has_training_docs(self): |
| return False |
|
|
| def has_validation_docs(self): |
| return True |
|
|
| def has_test_docs(self): |
| return False |
|
|
| def validation_docs(self): |
| |
| |
| |
| return self.dataset["train"] |
| |
| def null_prompt_doc_to_text(self, doc: dict) -> str: |
| return "" |
|
|
| def null_prompt_doc_to_target(self, doc: dict) -> List[str]: |
| correct_choice = doc["prefix_acceptable"] |
| sentence_end = doc["property_phrase"] |
| formatted = f"{correct_choice} {sentence_end}" |
| return [formatted] |
| |
| def null_prompt_answer_choices(self, doc: dict) -> List[str]: |
| choices = [doc["prefix_acceptable"], doc["prefix_unacceptable"]] |
| sentence_end = doc["property_phrase"] |
| formatted = [f"{choice} {sentence_end}" for choice in choices] |
| return formatted |
| |
| def null_prompt_get_logging_info(self): |
| return { |
| "fixed_answer_choice_list": None, |
| "dataset_path": self.DATASET_PATH, |
| "dataset_name": self.DATASET_NAME, |
| "subset": self.SPLIT, |
| "prompt_name": None, |
| "prompt_id": None, |
| "prompt_jinja": None, |
| "prompt_original_task": f"{self.DATASET_PATH}/{self.DATASET_NAME}", |
| |
| "comment": "", |
| } |
|
|
|
|
| class CompsBase(CompsTask): |
| DATASET_NAME = "base" |
|
|
|
|
| class CompsWugs(CompsTask): |
| DATASET_NAME = "wugs" |
