@inproceedings{kim-etal-2026-steerlsn,
title = "{STEERLSN}: Training-Free Representation Alignment through Language-Specific Neuron Subspace Interpolation",
author = "Kim, Minjun and
Kim, Dongyeon and
Song, Hoyun and
Kim, Jihyo and
Lim, KyungTae",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {IJCNLP}-{AACL} 2026",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-aacl.51/",
pages = "1005--1018",
ISBN = "979-8-89176-444-6",
abstract = "Recent advancements in Large Language Models (LLMs) have shown remarkable capabilities in multilingual contexts. However, previous studies suggest that multilingual LLMs primarily rely on the rich semantic knowledge of high-resource (i.e., English) representations to conduct tasks across languages. To mitigate the performance gap arising from resource imbalances, we propose \textit{Steering via Language-Specific Neurons}, a training-free method that aligns non-English representations with the English manifold intervening on language-specific neuron activations via linear interpolation toward English activation prototypes. Experimental results show that the proposed method generally improves downstream task performance with minimal computational cost, yielding performance gains for low-resource scenarios where limited data often constrains knowledge coverage. Our findings suggest that the fundamental limitations of low-resource languages can be effectively alleviated by leveraging the robust representation space of high-resource languages, offering a promising direction for more balanced multilingual LLMs."
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<abstract>Recent advancements in Large Language Models (LLMs) have shown remarkable capabilities in multilingual contexts. However, previous studies suggest that multilingual LLMs primarily rely on the rich semantic knowledge of high-resource (i.e., English) representations to conduct tasks across languages. To mitigate the performance gap arising from resource imbalances, we propose Steering via Language-Specific Neurons, a training-free method that aligns non-English representations with the English manifold intervening on language-specific neuron activations via linear interpolation toward English activation prototypes. Experimental results show that the proposed method generally improves downstream task performance with minimal computational cost, yielding performance gains for low-resource scenarios where limited data often constrains knowledge coverage. Our findings suggest that the fundamental limitations of low-resource languages can be effectively alleviated by leveraging the robust representation space of high-resource languages, offering a promising direction for more balanced multilingual LLMs.</abstract>
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%0 Conference Proceedings
%T STEERLSN: Training-Free Representation Alignment through Language-Specific Neuron Subspace Interpolation
%A Kim, Minjun
%A Kim, Dongyeon
%A Song, Hoyun
%A Kim, Jihyo
%A Lim, KyungTae
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-444-6
%F kim-etal-2026-steerlsn
%X Recent advancements in Large Language Models (LLMs) have shown remarkable capabilities in multilingual contexts. However, previous studies suggest that multilingual LLMs primarily rely on the rich semantic knowledge of high-resource (i.e., English) representations to conduct tasks across languages. To mitigate the performance gap arising from resource imbalances, we propose Steering via Language-Specific Neurons, a training-free method that aligns non-English representations with the English manifold intervening on language-specific neuron activations via linear interpolation toward English activation prototypes. Experimental results show that the proposed method generally improves downstream task performance with minimal computational cost, yielding performance gains for low-resource scenarios where limited data often constrains knowledge coverage. Our findings suggest that the fundamental limitations of low-resource languages can be effectively alleviated by leveraging the robust representation space of high-resource languages, offering a promising direction for more balanced multilingual LLMs.
%U https://aclanthology.org/2026.findings-aacl.51/
%P 1005-1018
Markdown (Informal)
[STEERLSN: Training-Free Representation Alignment through Language-Specific Neuron Subspace Interpolation](https://aclanthology.org/2026.findings-aacl.51/) (Kim et al., Findings 2026)
ACL