Our team from XRI Global and the University of Arizona looked at what data and AI models exist for low-resource languages. We gathered info from places like Hugging Face and GitHub to see which languages need more support.
We created a map showing which low-resource languages have digital tools and which don’t. This helps us see where the gaps are and track progress as we try to include more languages in the digital world.
As more communication happens through technology, languages without digital support risk being left behind. Our work gives a starting point for helping more languages thrive technologically, making sure everyone can use their own language in the digital age.
We shared these findings at a conference in Paris and hope our work helps more languages get the tech support they need.
We’ll keep working to bridge the gap between high-resource and low-resource languages!
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