LangFIR: Discovering Sparse Language-Specific Features from Monolingual Data for Language Steering
Large language models (LLMs) show strong multilingual capabilities, yet reliably controlling the language of their outputs remains difficult. Representation-level steering addresses this by adding language-specific vectors to model activations at inference time, but identifying language-specific directions in the residual stream often relies on multilingual or parallel data that can be expensive to obtain. Sparse autoencoders (SAEs) decompose residual activations into interpretable, sparse feature directions and offer a natural basis for this search, yet existing SAE-based approaches face the same data constraint. We introduce LangFIR (Language Feature Identification via Random-token Filt...
| Authors | Sing Hieng Wong, Hassan Sajjad, A. B. Siddique |
|---|---|
| Primary category | cs.CL |
| Published | 4 April 2026 |
| Last updated at source | 2 August 2026 |
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