@inproceedings{zheng-etal-2026-unveiling,
title = "Unveiling Language Routing Isolation in Multilingual {M}o{E} Models for Interpretable Subnetwork Adaptation",
author = "Zheng, Kening and
Huang, Wei-Chieh and
Huo, Jiahao and
Li, Zhonghao and
Zou, Henry Peng and
Yan, Yibo and
Zou, Xin and
Li, Jungang and
Zhang, Hanrong and
Hu, Xuming and
Yu, Philip S.",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.283/",
pages = "5984--6004",
ISBN = "979-8-89176-462-0",
abstract = "Mixture-of-Experts (MoE) models exhibit striking performance disparities across languages, yet the internal mechanisms driving these gaps remain poorly understood. In this work, we conduct a systematic analysis of expert routing patterns in MoE models, revealing a phenomenon we term *Language Routing Isolation*, in which high- and low-resource languages tend to activate largely disjoint expert sets. Through layer-stratified analysis, we further show that routing patterns first converge and then diverge across model depth. Building on these findings, we propose **RISE** (routing isolation-guided subnetwork enhancement), a framework that identifies and adapts language-specific expert subnetworks. RISE uses a tripartite selection strategy: specificity scores select language-specific experts in shallow and deep layers, while overlap scores select universal experts in middle layers. By training only the selected subnetwork while freezing the rest of the model, RISE improves low-resource performance while preserving other-language capabilities. Across 10 languages, RISE achieves target-language F1 gains of up to 10.85{\%} with minimal cross-lingual degradation."
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<abstract>Mixture-of-Experts (MoE) models exhibit striking performance disparities across languages, yet the internal mechanisms driving these gaps remain poorly understood. In this work, we conduct a systematic analysis of expert routing patterns in MoE models, revealing a phenomenon we term *Language Routing Isolation*, in which high- and low-resource languages tend to activate largely disjoint expert sets. Through layer-stratified analysis, we further show that routing patterns first converge and then diverge across model depth. Building on these findings, we propose **RISE** (routing isolation-guided subnetwork enhancement), a framework that identifies and adapts language-specific expert subnetworks. RISE uses a tripartite selection strategy: specificity scores select language-specific experts in shallow and deep layers, while overlap scores select universal experts in middle layers. By training only the selected subnetwork while freezing the rest of the model, RISE improves low-resource performance while preserving other-language capabilities. Across 10 languages, RISE achieves target-language F1 gains of up to 10.85% with minimal cross-lingual degradation.</abstract>
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%0 Conference Proceedings
%T Unveiling Language Routing Isolation in Multilingual MoE Models for Interpretable Subnetwork Adaptation
%A Zheng, Kening
%A Huang, Wei-Chieh
%A Huo, Jiahao
%A Li, Zhonghao
%A Zou, Henry Peng
%A Yan, Yibo
%A Zou, Xin
%A Li, Jungang
%A Zhang, Hanrong
%A Hu, Xuming
%A Yu, Philip S.
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F zheng-etal-2026-unveiling
%X Mixture-of-Experts (MoE) models exhibit striking performance disparities across languages, yet the internal mechanisms driving these gaps remain poorly understood. In this work, we conduct a systematic analysis of expert routing patterns in MoE models, revealing a phenomenon we term *Language Routing Isolation*, in which high- and low-resource languages tend to activate largely disjoint expert sets. Through layer-stratified analysis, we further show that routing patterns first converge and then diverge across model depth. Building on these findings, we propose **RISE** (routing isolation-guided subnetwork enhancement), a framework that identifies and adapts language-specific expert subnetworks. RISE uses a tripartite selection strategy: specificity scores select language-specific experts in shallow and deep layers, while overlap scores select universal experts in middle layers. By training only the selected subnetwork while freezing the rest of the model, RISE improves low-resource performance while preserving other-language capabilities. Across 10 languages, RISE achieves target-language F1 gains of up to 10.85% with minimal cross-lingual degradation.
%U https://aclanthology.org/2026.findings-emnlp.283/
%P 5984-6004
Markdown (Informal)
[Unveiling Language Routing Isolation in Multilingual MoE Models for Interpretable Subnetwork Adaptation](https://aclanthology.org/2026.findings-emnlp.283/) (Zheng et al., Findings 2026)
ACL
- Kening Zheng, Wei-Chieh Huang, Jiahao Huo, Zhonghao Li, Henry Peng Zou, Yibo Yan, Xin Zou, Jungang Li, Hanrong Zhang, Xuming Hu, and Philip S. Yu. 2026. Unveiling Language Routing Isolation in Multilingual MoE Models for Interpretable Subnetwork Adaptation. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 5984–6004, Budapest, Hungary. Association for Computational Linguistics.