annotations_creators:
- other
language_creators:
- machine-generated
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- extended|glue
task_categories:
- text-classification
task_ids:
- natural-language-inference
- sentiment-classification
pretty_name: Adversarial GLUE
config_names:
- adv_mnli
- adv_mnli_mismatched
- adv_qnli
- adv_qqp
- adv_rte
- adv_sst2
tags:
- paraphrase-identification
- qa-nli
dataset_info:
- config_name: adv_mnli
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
- name: idx
dtype: int32
splits:
- name: validation
num_bytes: 23712
num_examples: 121
download_size: 13485
dataset_size: 23712
- config_name: adv_mnli_mismatched
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
- name: idx
dtype: int32
splits:
- name: validation
num_bytes: 40982
num_examples: 162
download_size: 40662
dataset_size: 40982
- config_name: adv_qnli
features:
- name: question
dtype: string
- name: sentence
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': not_entailment
- name: idx
dtype: int32
splits:
- name: validation
num_bytes: 34877
num_examples: 148
download_size: 40662
dataset_size: 34877
- config_name: adv_qqp
features:
- name: question1
dtype: string
- name: question2
dtype: string
- name: label
dtype:
class_label:
names:
'0': not_duplicate
'1': duplicate
- name: idx
dtype: int32
splits:
- name: validation
num_bytes: 9926
num_examples: 78
download_size: 40662
dataset_size: 9926
- config_name: adv_rte
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': not_entailment
- name: idx
dtype: int32
splits:
- name: validation
num_bytes: 25998
num_examples: 81
download_size: 40662
dataset_size: 25998
- config_name: adv_sst2
features:
- name: sentence
dtype: string
- name: label
dtype:
class_label:
names:
'0': negative
'1': positive
- name: idx
dtype: int32
splits:
- name: validation
num_bytes: 16595
num_examples: 148
download_size: 40662
dataset_size: 16595
configs:
- config_name: adv_mnli
data_files:
- split: validation
path: adv_mnli/validation-*
Dataset Card for Adversarial GLUE
Table of Contents
- Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://adversarialglue.github.io/
- Repository:
- Paper: arXiv
- Leaderboard:
- Point of Contact:
- Size of downloaded dataset files: 202.75 kB
Dataset Summary
Adversarial GLUE Benchmark (AdvGLUE) is a comprehensive robustness evaluation benchmark that focuses on the adversarial robustness evaluation of language models. It covers five natural language understanding tasks from the famous GLUE tasks and is an adversarial version of GLUE benchmark.
AdvGLUE considers textual adversarial attacks from different perspectives and hierarchies, including word-level transformations, sentence-level manipulations, and human-written adversarial examples, which provide comprehensive coverage of various adversarial linguistic phenomena.
Supported Tasks and Leaderboards
Leaderboard available on the homepage: https://adversarialglue.github.io/.
Languages
AdvGLUE deviates from the GLUE dataset, which has a base language of English.
Dataset Structure
Data Instances
default
- Size of downloaded dataset files: 202.75 kB
- Example:
>>> datasets.load_dataset('adv_glue', 'adv_sst2')['validation'][0]
{'sentence': "it 's an uneven treat that bores fun at the democratic exercise while also examining its significance for those who take part .", 'label': 1, 'idx': 0}
Data Fields
The data fields are the same as in the GLUE dataset, which differ by task.
The data fields are the same among all splits.
adv_mnli
premise: astringfeature.hypothesis: astringfeature.label: a classification label, with possible values includingentailment(0),neutral(1),contradiction(2).idx: aint32feature.
adv_mnli_matched
premise: astringfeature.hypothesis: astringfeature.label: a classification label, with possible values includingentailment(0),neutral(1),contradiction(2).idx: aint32feature.
adv_mnli_mismatched
premise: astringfeature.hypothesis: astringfeature.label: a classification label, with possible values includingentailment(0),neutral(1),contradiction(2).idx: aint32feature.
adv_qnli
adv_qqp
adv_rte
adv_sst2
Data Splits
Adversarial GLUE provides only a 'dev' split.
Dataset Creation
Curation Rationale
[More Information Needed]
Source Data
Initial Data Collection and Normalization
[More Information Needed]
Who are the source language producers?
[More Information Needed]
Annotations
Annotation process
[More Information Needed]
Who are the annotators?
[More Information Needed]
Personal and Sensitive Information
[More Information Needed]
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]
Additional Information
Dataset Curators
[More Information Needed]
Licensing Information
The dataset is distributed under the CC BY-SA 4.0 license.
Citation Information
@article{Wang2021AdversarialGA,
title={Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models},
author={Boxin Wang and Chejian Xu and Shuohang Wang and Zhe Gan and Yu Cheng and Jianfeng Gao and Ahmed Hassan Awadallah and B. Li},
journal={ArXiv},
year={2021},
volume={abs/2111.02840}
}
Contributions
Thanks to @jxmorris12 for adding this dataset.