Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model
This work addresses the challenge of broadening AI access for speakers of lower-resourced languages, representing a strong specific gain in multilingual NLP.
The paper tackles the problem of limited access to large language model breakthroughs for non-data-rich languages by introducing Aya, a multilingual instruction-following model covering 101 languages, over 50% of which are lower-resourced, and it outperforms mT0 and BLOOMZ on most tasks while doubling language coverage.
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