Title: `Few-shot Learning with Multilingual Language Models`
Abstract: `https://arxiv.org/abs/2112.10668`
XStoryCloze consists of the professionally translated version of the [English StoryCloze dataset](https://cs.rochester.edu/nlp/rocstories/)(Spring 2016 version) to 10 non-English languages. This dataset is released by Meta AI.
For adding novel benchmarks/datasets to the library:
* [ ] Is the task an existing benchmark in the literature?
* [ ] Have you referenced the original paper that introduced the task?
* [ ] If yes, does the original paper provide a reference implementation? If so, have you checked against the reference implementation and documented how to run such a test?
If other tasks on this dataset are already supported:
* [ ] Is the "Main" variant of this task clearly denoted?
* [ ] Have you provided a short sentence in a README on what each new variant adds / evaluates?
* [ ] Have you noted which, if any, published evaluation setups are matched by this variant?
Title: `SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference`
Abstract: https://arxiv.org/pdf/1808.05326.pdf
SWAG (Situations With Adversarial Generations) is an adversarial dataset
that consists of 113k multiple choice questions about grounded situations. Each
question is a video caption from LSMDC or ActivityNet Captions, with four answer
choices about what might happen next in the scene. The correct answer is the
(real) video caption for the next event in the video; the three incorrect
answers are adversarially generated and human verified, so as to fool machines
but not humans.
Homepage: https://rowanzellers.com/swag/
### Citation
```
@inproceedings{zellers2018swagaf,
title={SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference},
author={Zellers, Rowan and Bisk, Yonatan and Schwartz, Roy and Choi, Yejin},
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
year={2018}
}
```
### Groups and Tasks
#### Groups
* Not a part of a task yet.
#### Tasks
*`swag`
### Checklist
For adding novel benchmarks/datasets to the library:
* [ ] Is the task an existing benchmark in the literature?
* [ ] Have you referenced the original paper that introduced the task?
* [ ] If yes, does the original paper provide a reference implementation? If so, have you checked against the reference implementation and documented how to run such a test?
If other tasks on this dataset are already supported:
* [ ] Is the "Main" variant of this task clearly denoted?
* [ ] Have you provided a short sentence in a README on what each new variant adds / evaluates?
* [ ] Have you noted which, if any, published evaluation setups are matched by this variant?
Classify input text as either hateful or not hateful.
Homepage: https://github.com/microsoft/TOXIGEN
### Citation
```
@inproceedings{NEURIPS2020_1457c0d6,
author = {Brown, Tom and Mann, Benjamin and Ryder, Nick and Subbiah, Melanie and Kaplan, Jared D and Dhariwal, Prafulla and Neelakantan, Arvind and Shyam, Pranav and Sastry, Girish and Askell, Amanda and Agarwal, Sandhini and Herbert-Voss, Ariel and Krueger, Gretchen and Henighan, Tom and Child, Rewon and Ramesh, Aditya and Ziegler, Daniel and Wu, Jeffrey and Winter, Clemens and Hesse, Chris and Chen, Mark and Sigler, Eric and Litwin, Mateusz and Gray, Scott and Chess, Benjamin and Clark, Jack and Berner, Christopher and McCandlish, Sam and Radford, Alec and Sutskever, Ilya and Amodei, Dario},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin},
title={ToxiGen: A Large-Scale Machine-Generated Dataset for Implicit and Adversarial Hate Speech Detection},
author={Hartvigsen, Thomas and Gabriel, Saadia and Palangi, Hamid and Sap, Maarten and Ray, Dipankar and Kamar, Ece},
booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics},
year={2022}
}
```
### Subtasks
### Groups and Tasks
#### Groups
* Not part of a group yet.
#### Tasks
*`anagrams1` - Anagrams of all but the first and last letter.
*`anagrams2` - Anagrams of all but the first and last 2 letters.
*`cycle_letters` - Cycle letters in a word.
*`random_insertion` - Random insertions in the word that must be removed.
*`reversed_words` - Words spelled backwards that must be reversed.
*`toxigen`
### Checklist
For adding novel benchmarks/datasets to the library:
* [x] Is the task an existing benchmark in the literature?
* [x] Have you referenced the original paper that introduced the task?
* [x] If yes, does the original paper provide a reference implementation? If so, have you checked against the reference implementation and documented how to run such a test?
* [] Is the task an existing benchmark in the literature?
* [] Have you referenced the original paper that introduced the task?
* [] If yes, does the original paper provide a reference implementation? If so, have you checked against the reference implementation and documented how to run such a test?
If other tasks on this dataset are already supported:
* [x] Is the "Main" variant of this task clearly denoted?
* [x] Have you provided a short sentence in a README on what each new variant adds / evaluates?
* [x] Have you noted which, if any, published evaluation setups are matched by this variant?
* [x] Checked for equivalence with v0.3.0 LM Evaluation Harness
* [ ] Is the "Main" variant of this task clearly denoted?
* [ ] Have you provided a short sentence in a README on what each new variant adds / evaluates?
* [ ] Have you noted which, if any, published evaluation setups are matched by this variant?
Title: `TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension`
Abstract: https://arxiv.org/abs/1705.03551
TriviaQA is a reading comprehension dataset containing over 650K question-answer-evidence
triples. TriviaQA includes 95K question-answer pairs authored by trivia enthusiasts
and independently gathered evidence documents, six per question on average, that provide
high quality distant supervision for answering the questions.
Homepage: https://nlp.cs.washington.edu/triviaqa/
### Citation
```
@InProceedings{JoshiTriviaQA2017,
author = {Joshi, Mandar and Choi, Eunsol and Weld, Daniel S. and Zettlemoyer, Luke},
title = {TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension},
booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics},
month = {July},
year = {2017},
address = {Vancouver, Canada},
publisher = {Association for Computational Linguistics},
}
```
### Groups and Tasks
#### Groups
* Not part of a group yet.
#### Tasks
*`triviaqa`: `Generate and answer based on the question.`
### Checklist
For adding novel benchmarks/datasets to the library:
* [ ] Is the task an existing benchmark in the literature?
* [ ] Have you referenced the original paper that introduced the task?
* [ ] If yes, does the original paper provide a reference implementation? If so, have you checked against the reference implementation and documented how to run such a test?
If other tasks on this dataset are already supported:
* [ ] Is the "Main" variant of this task clearly denoted?
* [ ] Have you provided a short sentence in a README on what each new variant adds / evaluates?
* [ ] Have you noted which, if any, published evaluation setups are matched by this variant?
For adding novel benchmarks/datasets to the library:
* [ ] Is the task an existing benchmark in the literature?
* [ ] Have you referenced the original paper that introduced the task?
* [ ] If yes, does the original paper provide a reference implementation? If so, have you checked against the reference implementation and documented how to run such a test?
If other tasks on this dataset are already supported:
* [ ] Is the "Main" variant of this task clearly denoted?
* [ ] Have you provided a short sentence in a README on what each new variant adds / evaluates?
* [ ] Have you noted which, if any, published evaluation setups are matched by this variant?
