Zheng Yong


2024

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Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model
Ahmet Üstün | Viraat Aryabumi | Zheng Yong | Wei-Yin Ko | Daniel D’souza | Gbemileke Onilude | Neel Bhandari | Shivalika Singh | Hui-Lee Ooi | Amr Kayid | Freddie Vargus | Phil Blunsom | Shayne Longpre | Niklas Muennighoff | Marzieh Fadaee | Julia Kreutzer | Sara Hooker
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Recent breakthroughs in large language models (LLMs) have centered around a handful of data-rich languages. What does it take to broaden access to breakthroughs beyond first-class citizen languages? Our work introduces Aya, a massively multilingual generative language model that follows instructions in 101 languages of which over 50% are considered as lower-resourced. Aya outperforms mT0 and BLOOMZ on the majority of tasks while covering double the number of languages. We introduce extensive new evaluation suites that broaden the state-of-art for multilingual eval across 99 languages —— including discriminative and generative tasks, human evaluation, and simulated win rates that cover both held-out tasks and in-distribution performance. Furthermore, we conduct detailed investigations on the optimal finetuning mixture composition, data pruning, as well as the toxicity, bias, and safety of our models.