
# Dataset Card for BIG-bench

## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage/Repository:** [https://github.com/google/BIG-bench](https://github.com/google/BIG-bench)
- **Paper:** [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://arxiv.org/abs/2206.04615)
- **Leaderboard:**
- **Point of Contact:** [bigbench@googlegroups.com](mailto:bigbench@googlegroups.com)


### Dataset Summary

The Beyond the Imitation Game Benchmark (BIG-bench) is a collaborative benchmark intended to probe large language models and extrapolate their future capabilities. Tasks included in BIG-bench are summarized by keyword [here](https://github.com/google/BIG-bench/blob/main/bigbench/benchmark_tasks/keywords_to_tasks.md), and by task name [here](https://github.com/google/BIG-bench/blob/main/bigbench/benchmark_tasks/README.md). A paper introducing the benchmark, including evaluation results on large language models, is currently in preparation.

### Supported Tasks and Leaderboards

BIG-Bench consists of both json and programmatic tasks.
This implementation in HuggingFace datasets implements

  - 24 BIG-bench Lite tasks

  - 167 BIG-bench json tasks (includes BIG-bench Lite)
   
To study the remaining programmatic tasks, please see the [BIG-bench GitHub repo](https://github.com/google/BIG-bench)

### Languages

Although predominantly English, BIG-bench contains tasks in over 1000 written languages, as well as some synthetic and programming languages. 
See [BIG-bench organized by keywords](https://github.com/google/BIG-bench/blob/main/bigbench/benchmark_tasks/keywords_to_tasks.md). Relevant keywords include `multilingual`, `non-english`, `low-resource-language`, `translation`.

For tasks specifically targeting low-resource languages, see the table below:

Task Name | Languages |
--|--|
Conlang Translation Problems | English, German, Finnish, Abma, Apinayé, Inapuri, Ndebele, Palauan|
Kannada Riddles | Kannada|
Language Identification | 1000 languages |
Swahili English Proverbs | Swahili |
Which Wiki Edit | English, Russian, Spanish, German, French, Turkish, Japanese, Vietnamese, Chinese, Arabic, Norwegian, Tagalog|




## Dataset Structure

### Data Instances

Each dataset contains 5 features. For example an instance from the `emoji_movie` task is:

```
{
  "idx": 0,
  "inputs": "Q: What movie does this emoji describe? 👦👓⚡️\n  choice: harry potter\n. choice: shutter island\n. choice: inglourious basterds\n. choice: die hard\n. choice: moonlight\nA:"
  "targets": ["harry potter"],
  "multiple_choice_targets":["harry potter", "shutter island", "die hard", "inglourious basterds", "moonlight"],
  "multiple_choice_scores": [1, 0, 0, 0, 0]
}
```
      
For tasks that do not have multiple choice targets, the lists are empty.


### Data Fields

Every example has the following fields
  - `idx`: an `int` feature
  - `inputs`: a `string` feature
  - `targets`: a sequence of `string` feature
  - `multiple_choice_targets`: a sequence of `string` features
  - `multiple_choice_scores`: a sequence of `int` features

### Data Splits

Each task has a `default`, `train` and `validation` split.
The split `default` uses all the samples for each task (and it's the same as `all` used in the `bigbench.bbseqio` implementation.)
For standard evaluation on BIG-bench, we recommend using the `default` split, and the `train` and `validation` split is to be used if one wants to train a model on BIG-bench.

## Dataset Creation

BIG-bench tasks were collaboratively submitted through GitHub pull requests. 

Each task went through a review and meta-review process with criteria outlined in the [BIG-bench repository documentation](https://github.com/google/BIG-bench/blob/main/docs/doc.md#submission-review-process).
Each task was required to describe the data source and curation methods on the task README page. 

### 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

BIG-bench contains a wide range of tasks, some of which are sensitive and should be used with care.

Some tasks are specifically designed to test biases and failures common to large language models, and so may elicit inappropriate or harmful responses.
For a more thorough discussion see the [BIG-bench paper](in progress). 

To view tasks designed to probe pro-social behavior, including alignment, social, racial, gender, religious or political bias; toxicity; inclusion; and other issues please see tasks under the [pro-social behavior keywords](https://github.com/google/BIG-bench/blob/main/bigbench/benchmark_tasks/keywords_to_tasks.md#pro-social-behavior) on the BIG-bench repository.


### Social Impact of Dataset

[More Information Needed]


### Discussion of Biases

[More Information Needed]


### Other Known Limitations

[More Information Needed]


## Additional Information

For a more thorough discussion of all aspects of BIG-bench including dataset creation and evaluations see the BIG-bench repository [https://github.com/google/BIG-bench](https://github.com/google/BIG-bench) and paper []

### Dataset Curators

[More Information Needed]


### Licensing Information

[Apache License 2.0](https://github.com/google/BIG-bench/blob/main/LICENSE)

### Citation Information

```
@misc{https://doi.org/10.48550/arxiv.2206.04615,
  doi = {10.48550/ARXIV.2206.04615},
  url = {https://arxiv.org/abs/2206.04615},
  author = {Srivastava, Aarohi and Rastogi, Abhinav and Rao, Abhishek and Shoeb, Abu Awal Md and Abid, Abubakar and Fisch, Adam and Brown, Adam R. and Santoro, Adam and Gupta, Aditya and Garriga-Alonso, Adrià and Kluska, Agnieszka and Lewkowycz, Aitor and Agarwal, Akshat and Power, Alethea and Ray, Alex and Warstadt, Alex and Kocurek, Alexander W. and Safaya, Ali and Tazarv, Ali and Xiang, Alice and Parrish, Alicia and Nie, Allen and Hussain, Aman and Askell, Amanda and Dsouza, Amanda and Slone, Ambrose and Rahane, Ameet and Iyer, Anantharaman S. and Andreassen, Anders and Madotto, Andrea and Santilli, Andrea and Stuhlmüller, Andreas and Dai, Andrew and La, Andrew and Lampinen, Andrew and Zou, Andy and Jiang, Angela and Chen, Angelica and Vuong, Anh and Gupta, Animesh and Gottardi, Anna and Norelli, Antonio and Venkatesh, Anu and Gholamidavoodi, Arash and Tabassum, Arfa and Menezes, Arul and Kirubarajan, Arun and Mullokandov, Asher and Sabharwal, Ashish and Herrick, Austin and Efrat, Avia and Erdem, Aykut and Karakaş, Ayla and Roberts, B. Ryan and Loe, Bao Sheng and Zoph, Barret and Bojanowski, Bartłomiej and Özyurt, Batuhan and Hedayatnia, Behnam and Neyshabur, Behnam and Inden, Benjamin and Stein, Benno and Ekmekci, Berk and Lin, Bill Yuchen and Howald, Blake and Diao, Cameron and Dour, Cameron and Stinson, Catherine and Argueta, Cedrick and Ramírez, César Ferri and Singh, Chandan and Rathkopf, Charles and Meng, Chenlin and Baral, Chitta and Wu, Chiyu and Callison-Burch, Chris and Waites, Chris and Voigt, Christian and Manning, Christopher D. and Potts, Christopher and Ramirez, Cindy and Rivera, Clara E. and Siro, Clemencia and Raffel, Colin and Ashcraft, Courtney and Garbacea, Cristina and Sileo, Damien and Garrette, Dan and Hendrycks, Dan and Kilman, Dan and Roth, Dan and Freeman, Daniel and Khashabi, Daniel and Levy, Daniel and González, Daniel Moseguí and Perszyk, Danielle and Hernandez, Danny and Chen, Danqi and Ippolito, Daphne and Gilboa, Dar and Dohan, David and Drakard, David and Jurgens, David and Datta, Debajyoti and Ganguli, Deep and Emelin, Denis and Kleyko, Denis and Yuret, Deniz and Chen, Derek and Tam, Derek and Hupkes, Dieuwke and Misra, Diganta and Buzan, Dilyar and Mollo, Dimitri Coelho and Yang, Diyi and Lee, Dong-Ho and Shutova, Ekaterina and Cubuk, Ekin Dogus and Segal, Elad and Hagerman, Eleanor and Barnes, Elizabeth and Donoway, Elizabeth and Pavlick, Ellie and Rodola, Emanuele and Lam, Emma and Chu, Eric and Tang, Eric and Erdem, Erkut and Chang, Ernie and Chi, Ethan A. and Dyer, Ethan and Jerzak, Ethan and Kim, Ethan and Manyasi, Eunice Engefu and Zheltonozhskii, Evgenii and Xia, Fanyue and Siar, Fatemeh and Martínez-Plumed, Fernando and Happé, Francesca and Chollet, Francois and Rong, Frieda and Mishra, Gaurav and Winata, Genta Indra and de Melo, Gerard and Kruszewski, Germán and Parascandolo, Giambattista and Mariani, Giorgio and Wang, Gloria and Jaimovitch-López, Gonzalo and Betz, Gregor and Gur-Ari, Guy and Galijasevic, Hana and Kim, Hannah and Rashkin, Hannah and Hajishirzi, Hannaneh and Mehta, Harsh and Bogar, Hayden and Shevlin, Henry and Schütze, Hinrich and Yakura, Hiromu and Zhang, Hongming and Wong, Hugh Mee and Ng, Ian and Noble, Isaac and Jumelet, Jaap and Geissinger, Jack and Kernion, Jackson and Hilton, Jacob and Lee, Jaehoon and Fisac, Jaime Fernández and Simon, James B. and Koppel, James and Zheng, James and Zou, James and Kocoń, Jan and Thompson, Jana and Kaplan, Jared and Radom, Jarema and Sohl-Dickstein, Jascha and Phang, Jason and Wei, Jason and Yosinski, Jason and Novikova, Jekaterina and Bosscher, Jelle and Marsh, Jennifer and Kim, Jeremy and Taal, Jeroen and Engel, Jesse and Alabi, Jesujoba and Xu, Jiacheng and Song, Jiaming and Tang, Jillian and Waweru, Joan and Burden, John and Miller, John and Balis, John U. and Berant, Jonathan and Frohberg, Jörg and Rozen, Jos and Hernandez-Orallo, Jose and Boudeman, Joseph and Jones, Joseph and Tenenbaum, Joshua B. and Rule, Joshua S. and Chua, Joyce and Kanclerz, Kamil and Livescu, Karen and Krauth, Karl and Gopalakrishnan, Karthik and Ignatyeva, Katerina and Markert, Katja and Dhole, Kaustubh D. and Gimpel, Kevin and Omondi, Kevin and Mathewson, Kory and Chiafullo, Kristen and Shkaruta, Ksenia and Shridhar, Kumar and McDonell, Kyle and Richardson, Kyle and Reynolds, Laria and Gao, Leo and Zhang, Li and Dugan, Liam and Qin, Lianhui and Contreras-Ochando, Lidia and Morency, Louis-Philippe and Moschella, Luca and Lam, Lucas and Noble, Lucy and Schmidt, Ludwig and He, Luheng and Colón, Luis Oliveros and Metz, Luke and Şenel, Lütfi Kerem and Bosma, Maarten and Sap, Maarten and ter Hoeve, Maartje and Farooqi, Maheen and Faruqui, Manaal and Mazeika, Mantas and Baturan, Marco and Marelli, Marco and Maru, Marco and Quintana, Maria Jose Ramírez and Tolkiehn, Marie and Giulianelli, Mario and Lewis, Martha and Potthast, Martin and Leavitt, Matthew L. and Hagen, Matthias and Schubert, Mátyás and Baitemirova, Medina Orduna and Arnaud, Melody and McElrath, Melvin and Yee, Michael A. and Cohen, Michael and Gu, Michael and Ivanitskiy, Michael and Starritt, Michael and Strube, Michael and Swędrowski, Michał and Bevilacqua, Michele and Yasunaga, Michihiro and Kale, Mihir and Cain, Mike and Xu, Mimee and Suzgun, Mirac and Tiwari, Mo and Bansal, Mohit and Aminnaseri, Moin and Geva, Mor and Gheini, Mozhdeh and T, Mukund Varma and Peng, Nanyun and Chi, Nathan and Lee, Nayeon and Krakover, Neta Gur-Ari and Cameron, Nicholas and Roberts, Nicholas and Doiron, Nick and Nangia, Nikita and Deckers, Niklas and Muennighoff, Niklas and Keskar, Nitish Shirish and Iyer, Niveditha S. and Constant, Noah and Fiedel, Noah and Wen, Nuan and Zhang, Oliver and Agha, Omar and Elbaghdadi, Omar and Levy, Omer and Evans, Owain and Casares, Pablo Antonio Moreno and Doshi, Parth and Fung, Pascale and Liang, Paul Pu and Vicol, Paul and Alipoormolabashi, Pegah and Liao, Peiyuan and Liang, Percy and Chang, Peter and Eckersley, Peter and Htut, Phu Mon and Hwang, Pinyu and Miłkowski, Piotr and Patil, Piyush and Pezeshkpour, Pouya and Oli, Priti and Mei, Qiaozhu and Lyu, Qing and Chen, Qinlang and Banjade, Rabin and Rudolph, Rachel Etta and Gabriel, Raefer and Habacker, Rahel and Delgado, Ramón Risco and Millière, Raphaël and Garg, Rhythm and Barnes, Richard and Saurous, Rif A. and Arakawa, Riku and Raymaekers, Robbe and Frank, Robert and Sikand, Rohan and Novak, Roman and Sitelew, Roman and LeBras, Ronan and Liu, Rosanne and Jacobs, Rowan and Zhang, Rui and Salakhutdinov, Ruslan and Chi, Ryan and Lee, Ryan and Stovall, Ryan and Teehan, Ryan and Yang, Rylan and Singh, Sahib and Mohammad, Saif M. and Anand, Sajant and Dillavou, Sam and Shleifer, Sam and Wiseman, Sam and Gruetter, Samuel and Bowman, Samuel R. and Schoenholz, Samuel S. and Han, Sanghyun and Kwatra, Sanjeev and Rous, Sarah A. and Ghazarian, Sarik and Ghosh, Sayan and Casey, Sean and Bischoff, Sebastian and Gehrmann, Sebastian and Schuster, Sebastian and Sadeghi, Sepideh and Hamdan, Shadi and Zhou, Sharon and Srivastava, Shashank and Shi, Sherry and Singh, Shikhar and Asaadi, Shima and Gu, Shixiang Shane and Pachchigar, Shubh and Toshniwal, Shubham and Upadhyay, Shyam and Shyamolima,  and {Debnath} and Shakeri, Siamak and Thormeyer, Simon and Melzi, Simone and Reddy, Siva and Makini, Sneha Priscilla and Lee, Soo-Hwan and Torene, Spencer and Hatwar, Sriharsha and Dehaene, Stanislas and Divic, Stefan and Ermon, Stefano and Biderman, Stella and Lin, Stephanie and Prasad, Stephen and Piantadosi, Steven T. and Shieber, Stuart M. and Misherghi, Summer and Kiritchenko, Svetlana and Mishra, Swaroop and Linzen, Tal and Schuster, Tal and Li, Tao and Yu, Tao and Ali, Tariq and Hashimoto, Tatsu and Wu, Te-Lin and Desbordes, Théo and Rothschild, Theodore and Phan, Thomas and Wang, Tianle and Nkinyili, Tiberius and Schick, Timo and Kornev, Timofei and Telleen-Lawton, Timothy and Tunduny, Titus and Gerstenberg, Tobias and Chang, Trenton and Neeraj, Trishala and Khot, Tushar and Shultz, Tyler and Shaham, Uri and Misra, Vedant and Demberg, Vera and Nyamai, Victoria and Raunak, Vikas and Ramasesh, Vinay and Prabhu, Vinay Uday and Padmakumar, Vishakh and Srikumar, Vivek and Fedus, William and Saunders, William and Zhang, William and Vossen, Wout and Ren, Xiang and Tong, Xiaoyu and Zhao, Xinran and Wu, Xinyi and Shen, Xudong and Yaghoobzadeh, Yadollah and Lakretz, Yair and Song, Yangqiu and Bahri, Yasaman and Choi, Yejin and Yang, Yichi and Hao, Yiding and Chen, Yifu and Belinkov, Yonatan and Hou, Yu and Hou, Yufang and Bai, Yuntao and Seid, Zachary and Zhao, Zhuoye and Wang, Zijian and Wang, Zijie J. and Wang, Zirui and Wu, Ziyi},
  title = {Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models},
  publisher = {arXiv},
  year = {2022},
  copyright = {arXiv.org perpetual, non-exclusive license}
}
```

### Contributions
For a full list of contributors to the BIG-bench dataset, see the paper.

Thanks to [@andersjohanandreassen](https://github.com/andersjohanandreassen) and [@ethansdyer](https://github.com/ethansdyer) for adding this dataset to HuggingFace.
task_categories:multiple-choice task_categories:question-answering task_categories:text-classification task_categories:text-generation task_categories:zero-shot-classification task_categories:other task_ids:multiple-choice-qa task_ids:extractive-qa task_ids:open-domain-qa task_ids:closed-domain-qa task_ids:fact-checking task_ids:acceptability-classification task_ids:intent-classification task_ids:multi-class-classification task_ids:multi-label-classification task_ids:text-scoring task_ids:hate-speech-detection task_ids:language-modeling annotations_creators:crowdsourced annotations_creators:expert-generated annotations_creators:machine-generated language_creators:crowdsourced language_creators:expert-generated language_creators:machine-generated language_creators:other multilinguality:multilingual multilinguality:monolingual size_categories:unknown source_datasets:original language:en license:apache-2.0 arxiv:2206.04615 region:us
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TAGS
#task_categories-multiple-choice #task_categories-question-answering #task_categories-text-classification #task_categories-text-generation #task_categories-zero-shot-classification #task_categories-other #task_ids-multiple-choice-qa #task_ids-extractive-qa #task_ids-open-domain-qa #task_ids-closed-domain-qa #task_ids-fact-checking #task_ids-acceptability-classification #task_ids-intent-classification #task_ids-multi-class-classification #task_ids-multi-label-classification #task_ids-text-scoring #task_ids-hate-speech-detection #task_ids-language-modeling #annotations_creators-crowdsourced #annotations_creators-expert-generated #annotations_creators-machine-generated #language_creators-crowdsourced #language_creators-expert-generated #language_creators-machine-generated #language_creators-other #multilinguality-multilingual #multilinguality-monolingual #size_categories-unknown #source_datasets-original #language-English #license-apache-2.0 #arxiv-2206.04615 #region-us 

Dataset Card for BIG-bench
==========================


Table of Contents
-----------------


* Table of Contents
* Dataset Description
	+ Dataset Summary
	+ Supported Tasks and Leaderboards
	+ Languages
* Dataset Structure
	+ Data Instances
	+ Data Fields
	+ Data Splits
* Dataset Creation
	+ Curation Rationale
	+ Source Data
	+ Annotations
	+ 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
	+ Citation Information
	+ Contributions


Dataset Description
-------------------


* Homepage/Repository: URL
* Paper: Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
* Leaderboard:
* Point of Contact: bigbench@URL


### Dataset Summary


The Beyond the Imitation Game Benchmark (BIG-bench) is a collaborative benchmark intended to probe large language models and extrapolate their future capabilities. Tasks included in BIG-bench are summarized by keyword here, and by task name here. A paper introducing the benchmark, including evaluation results on large language models, is currently in preparation.


### Supported Tasks and Leaderboards


BIG-Bench consists of both json and programmatic tasks.
This implementation in HuggingFace datasets implements


* 24 BIG-bench Lite tasks
* 167 BIG-bench json tasks (includes BIG-bench Lite)


To study the remaining programmatic tasks, please see the BIG-bench GitHub repo


### Languages


Although predominantly English, BIG-bench contains tasks in over 1000 written languages, as well as some synthetic and programming languages.
See BIG-bench organized by keywords. Relevant keywords include 'multilingual', 'non-english', 'low-resource-language', 'translation'.


For tasks specifically targeting low-resource languages, see the table below:



Dataset Structure
-----------------


### Data Instances


Each dataset contains 5 features. For example an instance from the 'emoji\_movie' task is:


For tasks that do not have multiple choice targets, the lists are empty.


### Data Fields


Every example has the following fields


* 'idx': an 'int' feature
* 'inputs': a 'string' feature
* 'targets': a sequence of 'string' feature
* 'multiple\_choice\_targets': a sequence of 'string' features
* 'multiple\_choice\_scores': a sequence of 'int' features


### Data Splits


Each task has a 'default', 'train' and 'validation' split.
The split 'default' uses all the samples for each task (and it's the same as 'all' used in the 'bigbench.bbseqio' implementation.)
For standard evaluation on BIG-bench, we recommend using the 'default' split, and the 'train' and 'validation' split is to be used if one wants to train a model on BIG-bench.


Dataset Creation
----------------


BIG-bench tasks were collaboratively submitted through GitHub pull requests.


Each task went through a review and meta-review process with criteria outlined in the BIG-bench repository documentation.
Each task was required to describe the data source and curation methods on the task README page.


### 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
---------------------------------


BIG-bench contains a wide range of tasks, some of which are sensitive and should be used with care.


Some tasks are specifically designed to test biases and failures common to large language models, and so may elicit inappropriate or harmful responses.
For a more thorough discussion see the BIG-bench paper.


To view tasks designed to probe pro-social behavior, including alignment, social, racial, gender, religious or political bias; toxicity; inclusion; and other issues please see tasks under the pro-social behavior keywords on the BIG-bench repository.


### Social Impact of Dataset


### Discussion of Biases


### Other Known Limitations


Additional Information
----------------------


For a more thorough discussion of all aspects of BIG-bench including dataset creation and evaluations see the BIG-bench repository URL and paper []


### Dataset Curators


### Licensing Information


Apache License 2.0


### Contributions


For a full list of contributors to the BIG-bench dataset, see the paper.


Thanks to @andersjohanandreassen and @ethansdyer for adding this dataset to HuggingFace.



### Dataset Summary


The Beyond the Imitation Game Benchmark (BIG-bench) is a collaborative benchmark intended to probe large language models and extrapolate their future capabilities. Tasks included in BIG-bench are summarized by keyword here, and by task name here. A paper introducing the benchmark, including evaluation results on large language models, is currently in preparation. ### Supported Tasks and Leaderboards


BIG-Bench consists of both json and programmatic tasks.
This implementation in HuggingFace datasets implements


* 24 BIG-bench Lite tasks
* 167 BIG-bench json tasks (includes BIG-bench Lite)


To study the remaining programmatic tasks, please see the BIG-bench GitHub repo ### Languages


Although predominantly English, BIG-bench contains tasks in over 1000 written languages, as well as some synthetic and programming languages.
See BIG-bench organized by keywords. Relevant keywords include 'multilingual', 'non-english', 'low-resource-language', 'translation'.


For tasks specifically targeting low-resource languages, see the table below:



Dataset Structure
----------------- ### Data Instances


Each dataset contains 5 features. For example an instance from the 'emoji\_movie' task is:


For tasks that do not have multiple choice targets, the lists are empty. ### Data Fields


Every example has the following fields


* 'idx': an 'int' feature
* 'inputs': a 'string' feature
* 'targets': a sequence of 'string' feature
* 'multiple\_choice\_targets': a sequence of 'string' features
* 'multiple\_choice\_scores': a sequence of 'int' features ### Data Splits


Each task has a 'default', 'train' and 'validation' split.
The split 'default' uses all the samples for each task (and it's the same as 'all' used in the 'bigbench.bbseqio' implementation.)
For standard evaluation on BIG-bench, we recommend using the 'default' split, and the 'train' and 'validation' split is to be used if one wants to train a model on BIG-bench.


Dataset Creation
----------------


BIG-bench tasks were collaboratively submitted through GitHub pull requests.


Each task went through a review and meta-review process with criteria outlined in the BIG-bench repository documentation.
Each task was required to describe the data source and curation methods on the task README page. ### 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
---------------------------------


BIG-bench contains a wide range of tasks, some of which are sensitive and should be used with care.


Some tasks are specifically designed to test biases and failures common to large language models, and so may elicit inappropriate or harmful responses.
For a more thorough discussion see the BIG-bench paper.


To view tasks designed to probe pro-social behavior, including alignment, social, racial, gender, religious or political bias; toxicity; inclusion; and other issues please see tasks under the pro-social behavior keywords on the BIG-bench repository. ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Additional Information
----------------------


For a more thorough discussion of all aspects of BIG-bench including dataset creation and evaluations see the BIG-bench repository URL and paper [] ### Dataset Curators ### Licensing Information


Apache License 2.0 ### Contributions


For a full list of contributors to the BIG-bench dataset, see the paper.


Thanks to @andersjohanandreassen and @ethansdyer for adding this dataset to HuggingFace.
TAGS
#task_categories-multiple-choice #task_categories-question-answering #task_categories-text-classification #task_categories-text-generation #task_categories-zero-shot-classification #task_categories-other #task_ids-multiple-choice-qa #task_ids-extractive-qa #task_ids-open-domain-qa #task_ids-closed-domain-qa #task_ids-fact-checking #task_ids-acceptability-classification #task_ids-intent-classification #task_ids-multi-class-classification #task_ids-multi-label-classification #task_ids-text-scoring #task_ids-hate-speech-detection #task_ids-language-modeling #annotations_creators-crowdsourced #annotations_creators-expert-generated #annotations_creators-machine-generated #language_creators-crowdsourced #language_creators-expert-generated #language_creators-machine-generated #language_creators-other #multilinguality-multilingual #multilinguality-monolingual #size_categories-unknown #source_datasets-original #language-English #license-apache-2.0 #arxiv-2206.04615 #region-us 
 ### Dataset Summary


The Beyond the Imitation Game Benchmark (BIG-bench) is a collaborative benchmark intended to probe large language models and extrapolate their future capabilities. Tasks included in BIG-bench are summarized by keyword here, and by task name here. A paper introducing the benchmark, including evaluation results on large language models, is currently in preparation. ### Supported Tasks and Leaderboards


BIG-Bench consists of both json and programmatic tasks.
This implementation in HuggingFace datasets implements


* 24 BIG-bench Lite tasks
* 167 BIG-bench json tasks (includes BIG-bench Lite)


To study the remaining programmatic tasks, please see the BIG-bench GitHub repo ### Languages


Although predominantly English, BIG-bench contains tasks in over 1000 written languages, as well as some synthetic and programming languages.
See BIG-bench organized by keywords. Relevant keywords include 'multilingual', 'non-english', 'low-resource-language', 'translation'.


For tasks specifically targeting low-resource languages, see the table below:



Dataset Structure
----------------- ### Data Instances


Each dataset contains 5 features. For example an instance from the 'emoji\_movie' task is:


For tasks that do not have multiple choice targets, the lists are empty. ### Data Fields


Every example has the following fields


* 'idx': an 'int' feature
* 'inputs': a 'string' feature
* 'targets': a sequence of 'string' feature
* 'multiple\_choice\_targets': a sequence of 'string' features
* 'multiple\_choice\_scores': a sequence of 'int' features ### Data Splits


Each task has a 'default', 'train' and 'validation' split.
The split 'default' uses all the samples for each task (and it's the same as 'all' used in the 'bigbench.bbseqio' implementation.)
For standard evaluation on BIG-bench, we recommend using the 'default' split, and the 'train' and 'validation' split is to be used if one wants to train a model on BIG-bench.


Dataset Creation
----------------


BIG-bench tasks were collaboratively submitted through GitHub pull requests.


Each task went through a review and meta-review process with criteria outlined in the BIG-bench repository documentation.
Each task was required to describe the data source and curation methods on the task README page. ### 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
---------------------------------


BIG-bench contains a wide range of tasks, some of which are sensitive and should be used with care.


Some tasks are specifically designed to test biases and failures common to large language models, and so may elicit inappropriate or harmful responses.
For a more thorough discussion see the BIG-bench paper.


To view tasks designed to probe pro-social behavior, including alignment, social, racial, gender, religious or political bias; toxicity; inclusion; and other issues please see tasks under the pro-social behavior keywords on the BIG-bench repository. ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Additional Information
----------------------


For a more thorough discussion of all aspects of BIG-bench including dataset creation and evaluations see the BIG-bench repository URL and paper [] ### Dataset Curators ### Licensing Information


Apache License 2.0 ### Contributions


For a full list of contributors to the BIG-bench dataset, see the paper.


Thanks to @andersjohanandreassen and @ethansdyer for adding this dataset to HuggingFace.
passage: TAGS
#task_categories-multiple-choice #task_categories-question-answering #task_categories-text-classification #task_categories-text-generation #task_categories-zero-shot-classification #task_categories-other #task_ids-multiple-choice-qa #task_ids-extractive-qa #task_ids-open-domain-qa #task_ids-closed-domain-qa #task_ids-fact-checking #task_ids-acceptability-classification #task_ids-intent-classification #task_ids-multi-class-classification #task_ids-multi-label-classification #task_ids-text-scoring #task_ids-hate-speech-detection #task_ids-language-modeling #annotations_creators-crowdsourced #annotations_creators-expert-generated #annotations_creators-machine-generated #language_creators-crowdsourced #language_creators-expert-generated #language_creators-machine-generated #language_creators-other #multilinguality-multilingual #multilinguality-monolingual #size_categories-unknown #source_datasets-original #language-English #license-apache-2.0 #arxiv-2206.04615 #region-us 
### Dataset Summary


The Beyond the Imitation Game Benchmark (BIG-bench) is a collaborative benchmark intended to probe large language models and extrapolate their future capabilities. Tasks included in BIG-bench are summarized by keyword here, and by task name here. A paper introducing the benchmark, including evaluation results on large language models, is currently in preparation. passage: ### Supported Tasks and Leaderboards


BIG-Bench consists of both json and programmatic tasks.
This implementation in HuggingFace datasets implements


* 24 BIG-bench Lite tasks
* 167 BIG-bench json tasks (includes BIG-bench Lite)


To study the remaining programmatic tasks, please see the BIG-bench GitHub repo### Languages


Although predominantly English, BIG-bench contains tasks in over 1000 written languages, as well as some synthetic and programming languages.
See BIG-bench organized by keywords. Relevant keywords include 'multilingual', 'non-english', 'low-resource-language', 'translation'.


For tasks specifically targeting low-resource languages, see the table below:



Dataset Structure
-----------------### Data Instances


Each dataset contains 5 features. For example an instance from the 'emoji\_movie' task is:


For tasks that do not have multiple choice targets, the lists are empty.### Data Fields


Every example has the following fields


* 'idx': an 'int' feature
* 'inputs': a 'string' feature
* 'targets': a sequence of 'string' feature
* 'multiple\_choice\_targets': a sequence of 'string' features
* 'multiple\_choice\_scores': a sequence of 'int' features### Data Splits


Each task has a 'default', 'train' and 'validation' split.
The split 'default' uses all the samples for each task (and it's the same as 'all' used in the 'bigbench.bbseqio' implementation.)
For standard evaluation on BIG-bench, we recommend using the 'default' split, and the 'train' and 'validation' split is to be used if one wants to train a model on BIG-bench.


Dataset Creation
----------------


BIG-bench tasks were collaboratively submitted through GitHub pull requests.


Each task went through a review and meta-review process with criteria outlined in the BIG-bench repository documentation.
Each task was required to describe the data source and curation methods on the task README page.### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?