Multilingual Steering by Design: Multilingual Sparse Autoencoders and Principled Layer Selection
arXiv:2605.23036v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) enable feature-level mechanistic interpretability and activation steering in large language models (LLMs), but SAE-based language control remains unreliable in multilingual settings: most SAEs are trained on English-only data, and steering layers are chosen heuristically. We address these limitations by advancing a principled, mechanistic account of multilingual language steering with SAEs. First, we show that training SAEs on multilingual data consistently strengthens cross-lingual representations and yields more relia
The research addresses current limitations in AI steering within multilingual settings, driven by the increasing global deployment and integration of large language models.
Improving multilingual steering in LLMs enhances their reliability and applicability across diverse linguistic contexts, critical for global AI adoption and responsible development.
Mechanistic interpretability and control in LLMs become more robust for non-English languages, moving beyond English-centric AI development.
- · Multilingual AI developers
- · Non-English speaking users
- · AI governance & safety teams
- · LLM interpretability research
- · English-only LLM approaches
- · Heuristic layer selection methods
Multilingual LLMs gain increased trustworthiness and performance in diverse language tasks.
This leads to faster adoption and integration of AI into global markets and critical non-English applications.
Enhanced cross-lingual capabilities could reduce bias and improve equity in AI access and utility worldwide.
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Read at arXiv cs.CL