Aya Model: An Instruction Finetuned Open-Access Multilingual Language
  Model
Ahmet Üstün Viraat Aryabumi Zheng-Xin 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
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. We
open-source our instruction datasets and our model at
https://hf.co/CohereForAI/aya-101