@inproceedings{mahmoud-etal-2026-improving,
title = "Improving Multilingual Language Models by Aligning Representations through Steering",
author = "Mahmoud, Omar Mohamed and
Semage, Buddhika Laknath and
Karimpanal, Thommen George and
Rana, Santu",
editor = "Piperidis, Stelios and
Bel, N{\'u}ria and
van den Heuvel, Henk and
Ide, Nancy and
Krek, Simon and
Toral, Antonio",
booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "ELRA Language Resource Association",
url = "https://aclanthology.org/2026.lrec-1.164/",
doi = "10.63317/244dsoue8zu2",
pages = "2090--2103",
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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<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.</abstract>
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%0 Conference Proceedings
%T Improving Multilingual Language Models by Aligning Representations through Steering
%A Mahmoud, Omar Mohamed
%A Semage, Buddhika Laknath
%A Karimpanal, Thommen George
%A Rana, Santu
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F mahmoud-etal-2026-improving
%X 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.
%R 10.63317/244dsoue8zu2
%U https://aclanthology.org/2026.lrec-1.164/
%U https://doi.org/10.63317/244dsoue8zu2
%P 2090-2103
Markdown (Informal)
[Improving Multilingual Language Models by Aligning Representations through Steering](https://aclanthology.org/2026.lrec-1.164/) (Mahmoud et al., LREC 2026)
ACL