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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.06619 |
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| _version_ | 1866913229769277440 |
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| author | Singh, Shivalika Vargus, Freddie Dsouza, Daniel Karlsson, Börje F. Mahendiran, Abinaya Ko, Wei-Yin Shandilya, Herumb Patel, Jay Mataciunas, Deividas OMahony, Laura Zhang, Mike Hettiarachchi, Ramith Wilson, Joseph Machado, Marina Moura, Luisa Souza Krzemiński, Dominik Fadaei, Hakimeh Ergün, Irem Okoh, Ifeoma Alaagib, Aisha Mudannayake, Oshan Alyafeai, Zaid Chien, Vu Minh Ruder, Sebastian Guthikonda, Surya Alghamdi, Emad A. Gehrmann, Sebastian Muennighoff, Niklas Bartolo, Max Kreutzer, Julia Üstün, Ahmet Fadaee, Marzieh Hooker, Sara |
| author_facet | Singh, Shivalika Vargus, Freddie Dsouza, Daniel Karlsson, Börje F. Mahendiran, Abinaya Ko, Wei-Yin Shandilya, Herumb Patel, Jay Mataciunas, Deividas OMahony, Laura Zhang, Mike Hettiarachchi, Ramith Wilson, Joseph Machado, Marina Moura, Luisa Souza Krzemiński, Dominik Fadaei, Hakimeh Ergün, Irem Okoh, Ifeoma Alaagib, Aisha Mudannayake, Oshan Alyafeai, Zaid Chien, Vu Minh Ruder, Sebastian Guthikonda, Surya Alghamdi, Emad A. Gehrmann, Sebastian Muennighoff, Niklas Bartolo, Max Kreutzer, Julia Üstün, Ahmet Fadaee, Marzieh Hooker, Sara |
| contents | 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 finetuning 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 translating existing datasets across 114 languages. In total, we contribute four key resources: we develop and open-source the Aya Annotation Platform, 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 a valuable framework for future research collaborations that aim to bridge gaps in resources. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_06619 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning Singh, Shivalika Vargus, Freddie Dsouza, Daniel Karlsson, Börje F. Mahendiran, Abinaya Ko, Wei-Yin Shandilya, Herumb Patel, Jay Mataciunas, Deividas OMahony, Laura Zhang, Mike Hettiarachchi, Ramith Wilson, Joseph Machado, Marina Moura, Luisa Souza Krzemiński, Dominik Fadaei, Hakimeh Ergün, Irem Okoh, Ifeoma Alaagib, Aisha Mudannayake, Oshan Alyafeai, Zaid Chien, Vu Minh Ruder, Sebastian Guthikonda, Surya Alghamdi, Emad A. Gehrmann, Sebastian Muennighoff, Niklas Bartolo, Max Kreutzer, Julia Üstün, Ahmet Fadaee, Marzieh Hooker, Sara Computation and Language Artificial Intelligence 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 finetuning 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 translating existing datasets across 114 languages. In total, we contribute four key resources: we develop and open-source the Aya Annotation Platform, 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 a valuable framework for future research collaborations that aim to bridge gaps in resources. |
| title | Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2402.06619 |