
The 42× number that should change how you scope annotation projects
If you’ve worked on NLP for low-resource languages — Yoruba, Quechua, Tigrinya, Hmong — you know the math is brutal. Native speakers with domain expertise are scarce. Their time is
Booth 21-25 | AI Data Management Zone | Tokyo Big Sight

If you’ve worked on NLP for low-resource languages — Yoruba, Quechua, Tigrinya, Hmong — you know the math is brutal. Native speakers with domain expertise are scarce. Their time is

For most of the last six years, the most capable LLMs were locked behind APIs. You could call them, you could build on them, but you couldn’t look inside, customize the weights, or run them on your own infrastructure. That was simply the reality of working with frontier models. In
