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16 results on '"McMillan-Major, Angelina"'

1. The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset

Author

Laurençon, Hugo, Saulnier, Lucile, Wang, Thomas, Akiki, Christopher, del Moral, Albert Villanova, Scao, Teven Le, Von Werra, Leandro, Mou, Chenghao, Ponferrada, Eduardo González, Nguyen, Huu, Frohberg, Jörg, Šaško, Mario, Lhoest, Quentin, McMillan-Major, Angelina, Dupont, Gerard, Biderman, Stella, Rogers, Anna, allal, Loubna Ben, De Toni, Francesco, Pistilli, Giada, Nguyen, Olivier, Nikpoor, Somaieh, Masoud, Maraim, Colombo, Pierre, de la Rosa, Javier, Villegas, Paulo, Thrush, Tristan, Longpre, Shayne, Nagel, Sebastian, Weber, Leon, Muñoz, Manuel, Zhu, Jian, Van Strien, Daniel, Alyafeai, Zaid, Almubarak, Khalid, Vu, Minh Chien, Gonzalez-Dios, Itziar, Soroa, Aitor, Lo, Kyle, Dey, Manan, Suarez, Pedro Ortiz, Gokaslan, Aaron, Bose, Shamik, Adelani, David, Phan, Long, Tran, Hieu, Yu, Ian, Pai, Suhas, Chim, Jenny, Lepercq, Violette, Ilic, Suzana, Mitchell, Margaret, Luccioni, Sasha Alexandra, and Jernite, Yacine

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, I.2.7
Abstract

As language models grow ever larger, the need for large-scale high-quality text datasets has never been more pressing, especially in multilingual settings. The BigScience workshop, a 1-year international and multidisciplinary initiative, was formed with the goal of researching and training large language models as a values-driven undertaking, putting issues of ethics, harm, and governance in the foreground. This paper documents the data creation and curation efforts undertaken by BigScience to assemble the Responsible Open-science Open-collaboration Text Sources (ROOTS) corpus, a 1.6TB dataset spanning 59 languages that was used to train the 176-billion-parameter BigScience Large Open-science Open-access Multilingual (BLOOM) language model. We further release a large initial subset of the corpus and analyses thereof, and hope to empower large-scale monolingual and multilingual modeling projects with both the data and the processing tools, as well as stimulate research around this large multilingual corpus., Comment: NeurIPS 2022, Datasets and Benchmarks Track

Published
2023

2. Measuring Data

Author

Mitchell, Margaret, Luccioni, Alexandra Sasha, Lambert, Nathan, Gerchick, Marissa, McMillan-Major, Angelina, Ozoani, Ezinwanne, Rajani, Nazneen, Thrush, Tristan, Jernite, Yacine, and Kiela, Douwe

Subjects
Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

We identify the task of measuring data to quantitatively characterize the composition of machine learning data and datasets. Similar to an object's height, width, and volume, data measurements quantify different attributes of data along common dimensions that support comparison. Several lines of research have proposed what we refer to as measurements, with differing terminology; we bring some of this work together, particularly in fields of computer vision and language, and build from it to motivate measuring data as a critical component of responsible AI development. Measuring data aids in systematically building and analyzing machine learning (ML) data towards specific goals and gaining better control of what modern ML systems will learn. We conclude with a discussion of the many avenues of future work, the limitations of data measurements, and how to leverage these measurement approaches in research and practice.

Published
2022

3. BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Author

Workshop, BigScience, Scao, Teven Le, Fan, Angela, Akiki, Christopher, Pavlick, Ellie, Ilić, Suzana, Hesslow, Daniel, Castagné, Roman, Luccioni, Alexandra Sasha, Yvon, François, Gallé, Matthias, Tow, Jonathan, Rush, Alexander M., Biderman, Stella, Webson, Albert, Ammanamanchi, Pawan Sasanka, Wang, Thomas, Sagot, Benoît, Muennighoff, Niklas, del Moral, Albert Villanova, Ruwase, Olatunji, Bawden, Rachel, Bekman, Stas, McMillan-Major, Angelina, Beltagy, Iz, Nguyen, Huu, Saulnier, Lucile, Tan, Samson, Suarez, Pedro Ortiz, Sanh, Victor, Laurençon, Hugo, Jernite, Yacine, Launay, Julien, Mitchell, Margaret, Raffel, Colin, Gokaslan, Aaron, Simhi, Adi, Soroa, Aitor, Aji, Alham Fikri, Alfassy, Amit, Rogers, Anna, Nitzav, Ariel Kreisberg, Xu, Canwen, Mou, Chenghao, Emezue, Chris, Klamm, Christopher, Leong, Colin, van Strien, Daniel, Adelani, David Ifeoluwa, Radev, Dragomir, Ponferrada, Eduardo González, Levkovizh, Efrat, Kim, Ethan, Natan, Eyal Bar, De Toni, Francesco, Dupont, Gérard, Kruszewski, Germán, Pistilli, Giada, Elsahar, Hady, Benyamina, Hamza, Tran, Hieu, Yu, Ian, Abdulmumin, Idris, Johnson, Isaac, Gonzalez-Dios, Itziar, de la Rosa, Javier, Chim, Jenny, Dodge, Jesse, Zhu, Jian, Chang, Jonathan, Frohberg, Jörg, Tobing, Joseph, Bhattacharjee, Joydeep, Almubarak, Khalid, Chen, Kimbo, Lo, Kyle, Von Werra, Leandro, Weber, Leon, Phan, Long, allal, Loubna Ben, Tanguy, Ludovic, Dey, Manan, Muñoz, Manuel Romero, Masoud, Maraim, Grandury, María, Šaško, Mario, Huang, Max, Coavoux, Maximin, Singh, Mayank, Jiang, Mike Tian-Jian, Vu, Minh Chien, Jauhar, Mohammad A., Ghaleb, Mustafa, Subramani, Nishant, Kassner, Nora, Khamis, Nurulaqilla, Nguyen, Olivier, Espejel, Omar, de Gibert, Ona, Villegas, Paulo, Henderson, Peter, Colombo, Pierre, Amuok, Priscilla, Lhoest, Quentin, Harliman, Rheza, Bommasani, Rishi, López, Roberto Luis, Ribeiro, Rui, Osei, Salomey, Pyysalo, Sampo, Nagel, Sebastian, Bose, Shamik, Muhammad, Shamsuddeen Hassan, Sharma, Shanya, Longpre, Shayne, Nikpoor, Somaieh, Silberberg, Stanislav, Pai, Suhas, Zink, Sydney, Torrent, Tiago Timponi, Schick, Timo, Thrush, Tristan, Danchev, Valentin, Nikoulina, Vassilina, Laippala, Veronika, Lepercq, Violette, Prabhu, Vrinda, Alyafeai, Zaid, Talat, Zeerak, Raja, Arun, Heinzerling, Benjamin, Si, Chenglei, Taşar, Davut Emre, Salesky, Elizabeth, Mielke, Sabrina J., Lee, Wilson Y., Sharma, Abheesht, Santilli, Andrea, Chaffin, Antoine, Stiegler, Arnaud, Datta, Debajyoti, Szczechla, Eliza, Chhablani, Gunjan, Wang, Han, Pandey, Harshit, Strobelt, Hendrik, Fries, Jason Alan, Rozen, Jos, Gao, Leo, Sutawika, Lintang, Bari, M Saiful, Al-shaibani, Maged S., Manica, Matteo, Nayak, Nihal, Teehan, Ryan, Albanie, Samuel, Shen, Sheng, Ben-David, Srulik, Bach, Stephen H., Kim, Taewoon, Bers, Tali, Fevry, Thibault, Neeraj, Trishala, Thakker, Urmish, Raunak, Vikas, Tang, Xiangru, Yong, Zheng-Xin, Sun, Zhiqing, Brody, Shaked, Uri, Yallow, Tojarieh, Hadar, Roberts, Adam, Chung, Hyung Won, Tae, Jaesung, Phang, Jason, Press, Ofir, Li, Conglong, Narayanan, Deepak, Bourfoune, Hatim, Casper, Jared, Rasley, Jeff, Ryabinin, Max, Mishra, Mayank, Zhang, Minjia, Shoeybi, Mohammad, Peyrounette, Myriam, Patry, Nicolas, Tazi, Nouamane, Sanseviero, Omar, von Platen, Patrick, Cornette, Pierre, Lavallée, Pierre François, Lacroix, Rémi, Rajbhandari, Samyam, Gandhi, Sanchit, Smith, Shaden, Requena, Stéphane, Patil, Suraj, Dettmers, Tim, Baruwa, Ahmed, Singh, Amanpreet, Cheveleva, Anastasia, Ligozat, Anne-Laure, Subramonian, Arjun, Névéol, Aurélie, Lovering, Charles, Garrette, Dan, Tunuguntla, Deepak, Reiter, Ehud, Taktasheva, Ekaterina, Voloshina, Ekaterina, Bogdanov, Eli, Winata, Genta Indra, Schoelkopf, Hailey, Kalo, Jan-Christoph, Novikova, Jekaterina, Forde, Jessica Zosa, Clive, Jordan, Kasai, Jungo, Kawamura, Ken, Hazan, Liam, Carpuat, Marine, Clinciu, Miruna, Kim, Najoung, Cheng, Newton, Serikov, Oleg, Antverg, Omer, van der Wal, Oskar, Zhang, Rui, Zhang, Ruochen, Gehrmann, Sebastian, Mirkin, Shachar, Pais, Shani, Shavrina, Tatiana, Scialom, Thomas, Yun, Tian, Limisiewicz, Tomasz, Rieser, Verena, Protasov, Vitaly, Mikhailov, Vladislav, Pruksachatkun, Yada, Belinkov, Yonatan, Bamberger, Zachary, Kasner, Zdeněk, Rueda, Alice, Pestana, Amanda, Feizpour, Amir, Khan, Ammar, Faranak, Amy, Santos, Ana, Hevia, Anthony, Unldreaj, Antigona, Aghagol, Arash, Abdollahi, Arezoo, Tammour, Aycha, HajiHosseini, Azadeh, Behroozi, Bahareh, Ajibade, Benjamin, Saxena, Bharat, Ferrandis, Carlos Muñoz, McDuff, Daniel, Contractor, Danish, Lansky, David, David, Davis, Kiela, Douwe, Nguyen, Duong A., Tan, Edward, Baylor, Emi, Ozoani, Ezinwanne, Mirza, Fatima, Ononiwu, Frankline, Rezanejad, Habib, Jones, Hessie, Bhattacharya, Indrani, Solaiman, Irene, Sedenko, Irina, Nejadgholi, Isar, Passmore, Jesse, Seltzer, Josh, Sanz, Julio Bonis, Dutra, Livia, Samagaio, Mairon, Elbadri, Maraim, Mieskes, Margot, Gerchick, Marissa, Akinlolu, Martha, McKenna, Michael, Qiu, Mike, Ghauri, Muhammed, Burynok, Mykola, Abrar, Nafis, Rajani, Nazneen, Elkott, Nour, Fahmy, Nour, Samuel, Olanrewaju, An, Ran, Kromann, Rasmus, Hao, Ryan, Alizadeh, Samira, Shubber, Sarmad, Wang, Silas, Roy, Sourav, Viguier, Sylvain, Le, Thanh, Oyebade, Tobi, Le, Trieu, Yang, Yoyo, Nguyen, Zach, Kashyap, Abhinav Ramesh, Palasciano, Alfredo, Callahan, Alison, Shukla, Anima, Miranda-Escalada, Antonio, Singh, Ayush, Beilharz, Benjamin, Wang, Bo, Brito, Caio, Zhou, Chenxi, Jain, Chirag, Xu, Chuxin, Fourrier, Clémentine, Periñán, Daniel León, Molano, Daniel, Yu, Dian, Manjavacas, Enrique, Barth, Fabio, Fuhrimann, Florian, Altay, Gabriel, Bayrak, Giyaseddin, Burns, Gully, Vrabec, Helena U., Bello, Imane, Dash, Ishani, Kang, Jihyun, Giorgi, John, Golde, Jonas, Posada, Jose David, Sivaraman, Karthik Rangasai, Bulchandani, Lokesh, Liu, Lu, Shinzato, Luisa, de Bykhovetz, Madeleine Hahn, Takeuchi, Maiko, Pàmies, Marc, Castillo, Maria A, Nezhurina, Marianna, Sänger, Mario, Samwald, Matthias, Cullan, Michael, Weinberg, Michael, De Wolf, Michiel, Mihaljcic, Mina, Liu, Minna, Freidank, Moritz, Kang, Myungsun, Seelam, Natasha, Dahlberg, Nathan, Broad, Nicholas Michio, Muellner, Nikolaus, Fung, Pascale, Haller, Patrick, Chandrasekhar, Ramya, Eisenberg, Renata, Martin, Robert, Canalli, Rodrigo, Su, Rosaline, Su, Ruisi, Cahyawijaya, Samuel, Garda, Samuele, Deshmukh, Shlok S, Mishra, Shubhanshu, Kiblawi, Sid, Ott, Simon, Sang-aroonsiri, Sinee, Kumar, Srishti, Schweter, Stefan, Bharati, Sushil, Laud, Tanmay, Gigant, Théo, Kainuma, Tomoya, Kusa, Wojciech, Labrak, Yanis, Bajaj, Yash Shailesh, Venkatraman, Yash, Xu, Yifan, Xu, Yingxin, Xu, Yu, Tan, Zhe, Xie, Zhongli, Ye, Zifan, Bras, Mathilde, Belkada, Younes, and Wolf, Thomas

Subjects
Computer Science - Computation and Language
Abstract

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

Published
2022

4. GEMv2: Multilingual NLG Benchmarking in a Single Line of Code

Author

Gehrmann, Sebastian, Bhattacharjee, Abhik, Mahendiran, Abinaya, Wang, Alex, Papangelis, Alexandros, Madaan, Aman, McMillan-Major, Angelina, Shvets, Anna, Upadhyay, Ashish, Yao, Bingsheng, Wilie, Bryan, Bhagavatula, Chandra, You, Chaobin, Thomson, Craig, Garbacea, Cristina, Wang, Dakuo, Deutsch, Daniel, Xiong, Deyi, Jin, Di, Gkatzia, Dimitra, Radev, Dragomir, Clark, Elizabeth, Durmus, Esin, Ladhak, Faisal, Ginter, Filip, Winata, Genta Indra, Strobelt, Hendrik, Hayashi, Hiroaki, Novikova, Jekaterina, Kanerva, Jenna, Chim, Jenny, Zhou, Jiawei, Clive, Jordan, Maynez, Joshua, Sedoc, João, Juraska, Juraj, Dhole, Kaustubh, Chandu, Khyathi Raghavi, Perez-Beltrachini, Laura, Ribeiro, Leonardo F. R., Tunstall, Lewis, Zhang, Li, Pushkarna, Mahima, Creutz, Mathias, White, Michael, Kale, Mihir Sanjay, Eddine, Moussa Kamal, Daheim, Nico, Subramani, Nishant, Dusek, Ondrej, Liang, Paul Pu, Ammanamanchi, Pawan Sasanka, Zhu, Qi, Puduppully, Ratish, Kriz, Reno, Shahriyar, Rifat, Cardenas, Ronald, Mahamood, Saad, Osei, Salomey, Cahyawijaya, Samuel, Štajner, Sanja, Montella, Sebastien, Shailza, Jolly, Shailza, Mille, Simon, Hasan, Tahmid, Shen, Tianhao, Adewumi, Tosin, Raunak, Vikas, Raheja, Vipul, Nikolaev, Vitaly, Tsai, Vivian, Jernite, Yacine, Xu, Ying, Sang, Yisi, Liu, Yixin, and Hou, Yufang

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Evaluation in machine learning is usually informed by past choices, for example which datasets or metrics to use. This standardization enables the comparison on equal footing using leaderboards, but the evaluation choices become sub-optimal as better alternatives arise. This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims. To make following best model evaluation practices easier, we introduce GEMv2. The new version of the Generation, Evaluation, and Metrics Benchmark introduces a modular infrastructure for dataset, model, and metric developers to benefit from each others work. GEMv2 supports 40 documented datasets in 51 languages. Models for all datasets can be evaluated online and our interactive data card creation and rendering tools make it easier to add new datasets to the living benchmark.

Published
2022

5. Documenting Geographically and Contextually Diverse Data Sources: The BigScience Catalogue of Language Data and Resources

Author

McMillan-Major, Angelina, Alyafeai, Zaid, Biderman, Stella, Chen, Kimbo, De Toni, Francesco, Dupont, Gérard, Elsahar, Hady, Emezue, Chris, Aji, Alham Fikri, Ilić, Suzana, Khamis, Nurulaqilla, Leong, Colin, Masoud, Maraim, Soroa, Aitor, Suarez, Pedro Ortiz, Talat, Zeerak, van Strien, Daniel, and Jernite, Yacine

Subjects
Computer Science - Computation and Language, Computer Science - Databases
Abstract

In recent years, large-scale data collection efforts have prioritized the amount of data collected in order to improve the modeling capabilities of large language models. This prioritization, however, has resulted in concerns with respect to the rights of data subjects represented in data collections, particularly when considering the difficulty in interrogating these collections due to insufficient documentation and tools for analysis. Mindful of these pitfalls, we present our methodology for a documentation-first, human-centered data collection project as part of the BigScience initiative. We identified a geographically diverse set of target language groups (Arabic, Basque, Chinese, Catalan, English, French, Indic languages, Indonesian, Niger-Congo languages, Portuguese, Spanish, and Vietnamese, as well as programming languages) for which to collect metadata on potential data sources. To structure this effort, we developed our online catalogue as a supporting tool for gathering metadata through organized public hackathons. We present our development process; analyses of the resulting resource metadata, including distributions over languages, regions, and resource types; and our lessons learned in this endeavor., Comment: 8 pages plus appendix and references

Published
2022

6. Datasets: A Community Library for Natural Language Processing

Author

Lhoest, Quentin, del Moral, Albert Villanova, Jernite, Yacine, Thakur, Abhishek, von Platen, Patrick, Patil, Suraj, Chaumond, Julien, Drame, Mariama, Plu, Julien, Tunstall, Lewis, Davison, Joe, Šaško, Mario, Chhablani, Gunjan, Malik, Bhavitvya, Brandeis, Simon, Scao, Teven Le, Sanh, Victor, Xu, Canwen, Patry, Nicolas, McMillan-Major, Angelina, Schmid, Philipp, Gugger, Sylvain, Delangue, Clément, Matussière, Théo, Debut, Lysandre, Bekman, Stas, Cistac, Pierric, Goehringer, Thibault, Mustar, Victor, Lagunas, François, Rush, Alexander M., and Wolf, Thomas

Subjects
Computer Science - Computation and Language
Abstract

The scale, variety, and quantity of publicly-available NLP datasets has grown rapidly as researchers propose new tasks, larger models, and novel benchmarks. Datasets is a community library for contemporary NLP designed to support this ecosystem. Datasets aims to standardize end-user interfaces, versioning, and documentation, while providing a lightweight front-end that behaves similarly for small datasets as for internet-scale corpora. The design of the library incorporates a distributed, community-driven approach to adding datasets and documenting usage. After a year of development, the library now includes more than 650 unique datasets, has more than 250 contributors, and has helped support a variety of novel cross-dataset research projects and shared tasks. The library is available at https://github.com/huggingface/datasets., Comment: EMNLP Demo 2021

Published
2021

7. Reusable Templates and Guides For Documenting Datasets and Models for Natural Language Processing and Generation: A Case Study of the HuggingFace and GEM Data and Model Cards

Author

McMillan-Major, Angelina, Osei, Salomey, Rodriguez, Juan Diego, Ammanamanchi, Pawan Sasanka, Gehrmann, Sebastian, and Jernite, Yacine

Subjects
Computer Science - Databases, Computer Science - Computation and Language
Abstract

Developing documentation guidelines and easy-to-use templates for datasets and models is a challenging task, especially given the variety of backgrounds, skills, and incentives of the people involved in the building of natural language processing (NLP) tools. Nevertheless, the adoption of standard documentation practices across the field of NLP promotes more accessible and detailed descriptions of NLP datasets and models, while supporting researchers and developers in reflecting on their work. To help with the standardization of documentation, we present two case studies of efforts that aim to develop reusable documentation templates -- the HuggingFace data card, a general purpose card for datasets in NLP, and the GEM benchmark data and model cards with a focus on natural language generation. We describe our process for developing these templates, including the identification of relevant stakeholder groups, the definition of a set of guiding principles, the use of existing templates as our foundation, and iterative revisions based on feedback., Comment: 15 pages; in Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021)

Published
2021
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8. The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics

Author

Gehrmann, Sebastian, Adewumi, Tosin, Aggarwal, Karmanya, Ammanamanchi, Pawan Sasanka, Anuoluwapo, Aremu, Bosselut, Antoine, Chandu, Khyathi Raghavi, Clinciu, Miruna, Das, Dipanjan, Dhole, Kaustubh D., Du, Wanyu, Durmus, Esin, Dušek, Ondřej, Emezue, Chris, Gangal, Varun, Garbacea, Cristina, Hashimoto, Tatsunori, Hou, Yufang, Jernite, Yacine, Jhamtani, Harsh, Ji, Yangfeng, Jolly, Shailza, Kale, Mihir, Kumar, Dhruv, Ladhak, Faisal, Madaan, Aman, Maddela, Mounica, Mahajan, Khyati, Mahamood, Saad, Majumder, Bodhisattwa Prasad, Martins, Pedro Henrique, McMillan-Major, Angelina, Mille, Simon, van Miltenburg, Emiel, Nadeem, Moin, Narayan, Shashi, Nikolaev, Vitaly, Niyongabo, Rubungo Andre, Osei, Salomey, Parikh, Ankur, Perez-Beltrachini, Laura, Rao, Niranjan Ramesh, Raunak, Vikas, Rodriguez, Juan Diego, Santhanam, Sashank, Sedoc, João, Sellam, Thibault, Shaikh, Samira, Shimorina, Anastasia, Cabezudo, Marco Antonio Sobrevilla, Strobelt, Hendrik, Subramani, Nishant, Xu, Wei, Yang, Diyi, Yerukola, Akhila, and Zhou, Jiawei

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics. Measuring progress in NLG relies on a constantly evolving ecosystem of automated metrics, datasets, and human evaluation standards. Due to this moving target, new models often still evaluate on divergent anglo-centric corpora with well-established, but flawed, metrics. This disconnect makes it challenging to identify the limitations of current models and opportunities for progress. Addressing this limitation, GEM provides an environment in which models can easily be applied to a wide set of tasks and in which evaluation strategies can be tested. Regular updates to the benchmark will help NLG research become more multilingual and evolve the challenge alongside models. This paper serves as the description of the data for which we are organizing a shared task at our ACL 2021 Workshop and to which we invite the entire NLG community to participate.

Published
2021

11. GEMv2: Multilingual NLG Benchmarking in a Single Line of Code

Author

Gehrmann, Sebastian, primary, Bhattacharjee, Abhik, additional, Mahendiran, Abinaya, additional, Wang, Alex, additional, Papangelis, Alexandros, additional, Madaan, Aman, additional, Mcmillan-major, Angelina, additional, Shvets, Anna, additional, Upadhyay, Ashish, additional, Bohnet, Bernd, additional, Yao, Bingsheng, additional, Wilie, Bryan, additional, Bhagavatula, Chandra, additional, You, Chaobin, additional, Thomson, Craig, additional, Garbacea, Cristina, additional, Wang, Dakuo, additional, Deutsch, Daniel, additional, Xiong, Deyi, additional, Jin, Di, additional, Gkatzia, Dimitra, additional, Radev, Dragomir, additional, Clark, Elizabeth, additional, Durmus, Esin, additional, Ladhak, Faisal, additional, Ginter, Filip, additional, Winata, Genta Indra, additional, Strobelt, Hendrik, additional, Hayashi, Hiroaki, additional, Novikova, Jekaterina, additional, Kanerva, Jenna, additional, Chim, Jenny, additional, Zhou, Jiawei, additional, Clive, Jordan, additional, Maynez, Joshua, additional, Sedoc, João, additional, Juraska, Juraj, additional, Dhole, Kaustubh, additional, Chandu, Khyathi Raghavi, additional, Beltrachini, Laura Perez, additional, Ribeiro, Leonardo F . R., additional, Tunstall, Lewis, additional, Zhang, Li, additional, Pushkarna, Mahim, additional, Creutz, Mathias, additional, White, Michael, additional, Kale, Mihir Sanjay, additional, Eddine, Moussa Kamal, additional, Daheim, Nico, additional, Subramani, Nishant, additional, Dusek, Ondrej, additional, Liang, Paul Pu, additional, Ammanamanchi, Pawan Sasanka, additional, Zhu, Qi, additional, Puduppully, Ratish, additional, Kriz, Reno, additional, Shahriyar, Rifat, additional, Cardenas, Ronald, additional, Mahamood, Saad, additional, Osei, Salomey, additional, Cahyawijaya, Samuel, additional, Štajner, Sanja, additional, Montella, Sebastien, additional, Jolly, Shailza, additional, Mille, Simon, additional, Hasan, Tahmid, additional, Shen, Tianhao, additional, Adewumi, Tosin, additional, Raunak, Vikas, additional, Raheja, Vipul, additional, Nikolaev, Vitaly, additional, Tsai, Vivian, additional, Jernite, Yacine, additional, Xu, Ying, additional, Sang, Yisi, additional, Liu, Yixin, additional, and Hou, Yufang, additional

Published
2022
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14. Datasets: A Community Library for Natural Language Processing

Author

Lhoest, Quentin, primary, Villanova del Moral, Albert, additional, Jernite, Yacine, additional, Thakur, Abhishek, additional, von Platen, Patrick, additional, Patil, Suraj, additional, Chaumond, Julien, additional, Drame, Mariama, additional, Plu, Julien, additional, Tunstall, Lewis, additional, Davison, Joe, additional, Šaško, Mario, additional, Chhablani, Gunjan, additional, Malik, Bhavitvya, additional, Brandeis, Simon, additional, Le Scao, Teven, additional, Sanh, Victor, additional, Xu, Canwen, additional, Patry, Nicolas, additional, McMillan-Major, Angelina, additional, Schmid, Philipp, additional, Gugger, Sylvain, additional, Delangue, Clément, additional, Matussière, Théo, additional, Debut, Lysandre, additional, Bekman, Stas, additional, Cistac, Pierric, additional, Goehringer, Thibault, additional, Mustar, Victor, additional, Lagunas, François, additional, Rush, Alexander, additional, and Wolf, Thomas, additional

Published
2021
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15. The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics

Author

Gehrmann, Sebastian, primary, Adewumi, Tosin, additional, Aggarwal, Karmanya, additional, Ammanamanchi, Pawan Sasanka, additional, Aremu, Anuoluwapo, additional, Bosselut, Antoine, additional, Chandu, Khyathi Raghavi, additional, Clinciu, Miruna-Adriana, additional, Das, Dipanjan, additional, Dhole, Kaustubh, additional, Du, Wanyu, additional, Durmus, Esin, additional, Dušek, Ondřej, additional, Emezue, Chris Chinenye, additional, Gangal, Varun, additional, Garbacea, Cristina, additional, Hashimoto, Tatsunori, additional, Hou, Yufang, additional, Jernite, Yacine, additional, Jhamtani, Harsh, additional, Ji, Yangfeng, additional, Jolly, Shailza, additional, Kale, Mihir, additional, Kumar, Dhruv, additional, Ladhak, Faisal, additional, Madaan, Aman, additional, Maddela, Mounica, additional, Mahajan, Khyati, additional, Mahamood, Saad, additional, Majumder, Bodhisattwa Prasad, additional, Martins, Pedro Henrique, additional, McMillan-Major, Angelina, additional, Mille, Simon, additional, van Miltenburg, Emiel, additional, Nadeem, Moin, additional, Narayan, Shashi, additional, Nikolaev, Vitaly, additional, Niyongabo Rubungo, Andre, additional, Osei, Salomey, additional, Parikh, Ankur, additional, Perez-Beltrachini, Laura, additional, Rao, Niranjan Ramesh, additional, Raunak, Vikas, additional, Rodriguez, Juan Diego, additional, Santhanam, Sashank, additional, Sedoc, João, additional, Sellam, Thibault, additional, Shaikh, Samira, additional, Shimorina, Anastasia, additional, Sobrevilla Cabezudo, Marco Antonio, additional, Strobelt, Hendrik, additional, Subramani, Nishant, additional, Xu, Wei, additional, Yang, Diyi, additional, Yerukola, Akhila, additional, and Zhou, Jiawei, additional

Published
2021
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