Aya Vision: Advancing the Frontier of Multilingual Multimodality
Saurabh Dash, Yiyang Nan, John Dang, Arash Ahmadian, Shivalika Singh, Madeline Smith, Bharat Venkitesh, Vlad Shmyhlo, Viraat Aryabumi, Walter Beller-Morales, Jeremy Pekmez, Jason Ozuzu, Pierre Richemond, Acyr Locatelli, Nick Frosst, Phil Blunsom, Aidan Gomez, Ivan Zhang, Marzieh Fadaee, Manoj Govindassamy, Sudip Roy, Matthias Gallé
2025-05-14
Summary
This paper talks about Aya Vision, a new way to make AI models better at understanding and working with both images and text in many different languages at the same time.
What's the problem?
The problem is that most AI models have trouble handling information that comes in different forms, like pictures and words, especially when that information is in multiple languages. This makes it hard for the models to be truly global and useful for everyone.
What's the solution?
The researchers improved these models by creating synthetic data, which means they generated extra training examples, and by using special techniques to merge information from images and text together. These changes helped the models do much better when working with different languages and types of data.
Why it matters?
This matters because it allows AI to help more people around the world, no matter what language they speak or whether they're using text or images, making technology more inclusive and powerful.
Abstract
Multi-modal language models are enhanced through synthetic data creation and cross-modal merging techniques, achieving superior performance in multilingual settings.