LangFIR: Discovering Sparse Language-Specific Features from Monolingual Data for Language Steering

Researchers introduce LangFIR, a method that enables better language control in multilingual AI models using only monolingual data instead of expensive parallel datasets. The technique identifies sparse language-specific features and achieves superior performance in controlling language output across multiple models including Gemma and Llama.