Improving Multilingual Language Models by Aligning Representations through Steering

Abstract

This paper investigates how Large Language Models (LLMs) represent non-English tokens—a question that remains underexplored despite recent progress. We propose a lightweight intervention method using representation steering, where a learned vector is added to the residual stream at a single model layer to enhance multilingual performance. Through extensive experiments across seven competitive baselines—including prompt optimization, supervised fine-tuning (SFT), in-context learning, cross-lingual transfer, projection mapping techniques, and translation-based methods—we show that our approach consistently outperforms most alternatives. In particular, it achieves performance on par with production-grade translation systems while requiring far fewer resources. We further explore the complementarity between our method and SFT, demonstrating that steering offers a direct, efficient way to realign internal representations. These findings underscore the potential of activation-level interventions as a powerful tool for improving the multilingual capabilities of LLMs.

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