Building Language Models for Low-Resource Languages with LLMs

Low-resource languages remain underrepresented in natural language processing because annotated corpora are scarce and expensive to produce. Large language models offer a practical escape hatch: they can generate synthetic training data, translate seed corpora, and evaluate intermediate outputs with minimal human supervision. The challenge is not whether LLMs can help, but how to run these workloads without costs ballooning as prompts grow.

Oxlo.ai provides a developer-first inference platform that removes the token counter from the equation