Booth 21-25 | AI Data Management Zone | Tokyo Big Sight
Before you reach for RLHF, before you design a reward model, before you start thinking about reinforcement learning from verifiable rewards — there’s a more fundamental question worth asking: has
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 expensive. The data needs are enormous. And you can’t bootstrap from a “close enough” language without inheriting serious bias. A
