@inproceedings{singh-etal-2024-aya,
title = "Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning",
author = {Singh, Shivalika and
Vargus, Freddie and
D{'}souza, Daniel and
Karlsson, B{\"o}rje and
Mahendiran, Abinaya and
Ko, Wei-Yin and
Shandilya, Herumb and
Patel, Jay and
Mataciunas, Deividas and
O{'}Mahony, Laura and
Zhang, Mike and
Hettiarachchi, Ramith and
Wilson, Joseph and
Machado, Marina and
Moura, Luisa and
Krzemi{\'n}ski, Dominik and
Fadaei, Hakimeh and
Ergun, Irem and
Okoh, Ifeoma and
Alaagib, Aisha and
Mudannayake, Oshan and
Alyafeai, Zaid and
Chien, Vu and
Ruder, Sebastian and
Guthikonda, Surya and
Alghamdi, Emad and
Gehrmann, Sebastian and
Muennighoff, Niklas and
Bartolo, Max and
Kreutzer, Julia and
{\"U}st{\"u}n, Ahmet and
Fadaee, Marzieh and
Hooker, Sara},
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.620",
doi = "10.18653/v1/2024.acl-long.620",
pages = "11521--11567",
abstract = "Datasets are foundational to many breakthroughs in modern artificial intelligence. Many recent achievements in the space of natural language processing (NLP) can be attributed to the fine-tuning of pre-trained models on a diverse set of tasks that enables a large language model (LLM) to respond to instructions. Instruction fine-tuning (IFT) requires specifically constructed and annotated datasets. However, existing datasets are almost all in the English language. In this work, our primary goal is to bridge the language gap by building a human-curated instruction-following dataset spanning 65 languages. We worked with fluent speakers of languages from around the world to collect natural instances of instructions and completions. Furthermore, we create the most extensive multilingual collection to date, comprising 513 million instances through templating and augmenting existing datasets across 114 languages. In total, we contribute three key resources: we develop and open-source the Aya Dataset, the Aya Collection, and the Aya Evaluation Suite. The Aya initiative also serves as a valuable case study in participatory research, involving collaborators from 119 countries. We see this as an important framework for future research collaborations that aim to bridge gaps in resources.",
}
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<abstract>Datasets are foundational to many breakthroughs in modern artificial intelligence. Many recent achievements in the space of natural language processing (NLP) can be attributed to the fine-tuning of pre-trained models on a diverse set of tasks that enables a large language model (LLM) to respond to instructions. Instruction fine-tuning (IFT) requires specifically constructed and annotated datasets. However, existing datasets are almost all in the English language. In this work, our primary goal is to bridge the language gap by building a human-curated instruction-following dataset spanning 65 languages. We worked with fluent speakers of languages from around the world to collect natural instances of instructions and completions. Furthermore, we create the most extensive multilingual collection to date, comprising 513 million instances through templating and augmenting existing datasets across 114 languages. In total, we contribute three key resources: we develop and open-source the Aya Dataset, the Aya Collection, and the Aya Evaluation Suite. The Aya initiative also serves as a valuable case study in participatory research, involving collaborators from 119 countries. We see this as an important framework for future research collaborations that aim to bridge gaps in resources.</abstract>
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%0 Conference Proceedings
%T Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning
%A Singh, Shivalika
%A Vargus, Freddie
%A D’souza, Daniel
%A Karlsson, Börje
%A Mahendiran, Abinaya
%A Ko, Wei-Yin
%A Shandilya, Herumb
%A Patel, Jay
%A Mataciunas, Deividas
%A O’Mahony, Laura
%A Zhang, Mike
%A Hettiarachchi, Ramith
%A Wilson, Joseph
%A Machado, Marina
%A Moura, Luisa
%A Krzemiński, Dominik
%A Fadaei, Hakimeh
%A Ergun, Irem
%A Okoh, Ifeoma
%A Alaagib, Aisha
%A Mudannayake, Oshan
%A Alyafeai, Zaid
%A Chien, Vu
%A Ruder, Sebastian
%A Guthikonda, Surya
%A Alghamdi, Emad
%A Gehrmann, Sebastian
%A Muennighoff, Niklas
%A Bartolo, Max
%A Kreutzer, Julia
%A Üstün, Ahmet
%A Fadaee, Marzieh
%A Hooker, Sara
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F singh-etal-2024-aya
%X Datasets are foundational to many breakthroughs in modern artificial intelligence. Many recent achievements in the space of natural language processing (NLP) can be attributed to the fine-tuning of pre-trained models on a diverse set of tasks that enables a large language model (LLM) to respond to instructions. Instruction fine-tuning (IFT) requires specifically constructed and annotated datasets. However, existing datasets are almost all in the English language. In this work, our primary goal is to bridge the language gap by building a human-curated instruction-following dataset spanning 65 languages. We worked with fluent speakers of languages from around the world to collect natural instances of instructions and completions. Furthermore, we create the most extensive multilingual collection to date, comprising 513 million instances through templating and augmenting existing datasets across 114 languages. In total, we contribute three key resources: we develop and open-source the Aya Dataset, the Aya Collection, and the Aya Evaluation Suite. The Aya initiative also serves as a valuable case study in participatory research, involving collaborators from 119 countries. We see this as an important framework for future research collaborations that aim to bridge gaps in resources.
%R 10.18653/v1/2024.acl-long.620
%U https://aclanthology.org/2024.acl-long.620
%U https://doi.org/10.18653/v1/2024.acl-long.620
%P 11521-11567
Markdown (Informal)
[Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning](https://aclanthology.org/2024.acl-long.620) (Singh et al., ACL 2024)
ACL
- Shivalika Singh, Freddie Vargus, Daniel D’souza, Börje Karlsson, Abinaya Mahendiran, Wei-Yin Ko, Herumb Shandilya, Jay Patel, Deividas Mataciunas, Laura O’Mahony, Mike Zhang, Ramith Hettiarachchi, Joseph Wilson, Marina Machado, Luisa Moura, Dominik Krzemiński, Hakimeh Fadaei, Irem Ergun, Ifeoma Okoh, et al.. 2024. Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 11521–11567, Bangkok, Thailand. Association for Computational Linguistics.
