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Unveiling Language Routing Isolation in Multilingual MoE Models for Interpretable Subnetwork Adaptation

arXiv:2604.03592v1 Announce Type: new 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 exhibit a layer-wise convergence-divergence pattern across model depth. Building on these findings, we propose RISE (Routing Isolation-guided Subnetwork Enhancement), a framework that exploits routing isolation to identify and adapt language-specific expert subnetworks. RISE applies a tripartite selection strategy, using specificity scores to identify language-specific experts in shallow and deep layers and overlap scores to select universal experts in middle layers. By tr

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Kening Zheng, Wei-Chieh Huang, Jiahao Huo, Zhonghao Li, Henry Peng Zou, Yibo Yan, Xin Zou, Jungang Li, Junzhuo Li, Hanrong Zhang, Xuming Hu, Philip S. Yu · · 1 min read · 4 views
#cs.CL #cs.AI

arXiv:2604.03592v1 Announce Type: new 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 exhibit a layer-wise convergence-divergence pattern across model depth. Building on these findings, we propose RISE (Routing Isolation-guided Subnetwork Enhancement), a framework that exploits routing isolation to identify and adapt language-specific expert subnetworks. RISE applies a tripartite selection strategy, using specificity scores to identify language-specific experts in shallow and deep layers and overlap scores to select universal experts in middle layers. By training only the selected subnetwork while freezing all other parameters, RISE substantially improves low-resource language performance while preserving capabilities in other languages. Experiments on 10 languages demonstrate that RISE achieves target-language F1 gains of up to 10.85% with minimal cross-lingual degradation.

Executive Summary

This article presents a systematic analysis of expert routing patterns in Mixture-of-Experts (MoE) models, revealing a phenomenon called Language Routing Isolation. The study shows that high- and low-resource languages tend to activate largely disjoint expert sets, with routing patterns exhibiting a layer-wise convergence-divergence pattern across model depth. Building on these findings, the authors propose RISE, a framework that exploits routing isolation to identify and adapt language-specific expert subnetworks. Experiments on 10 languages demonstrate that RISE achieves target-language F1 gains of up to 10.85% with minimal cross-lingual degradation. The study highlights the importance of understanding expert routing patterns in MoE models and proposes a practical framework for improving low-resource language performance.

Key Points

  • ▸ Language Routing Isolation is a phenomenon where high- and low-resource languages tend to activate largely disjoint expert sets in MoE models.
  • ▸ RISE is a framework that exploits routing isolation to identify and adapt language-specific expert subnetworks.
  • ▸ Experiments demonstrate that RISE achieves target-language F1 gains of up to 10.85% with minimal cross-lingual degradation.

Merits

Strength

The study provides a comprehensive analysis of expert routing patterns in MoE models, revealing a previously unknown phenomenon.

Strength

The proposed RISE framework is a practical and effective solution for improving low-resource language performance.

Demerits

Limitation

The study is limited to a specific type of MoE model and may not generalize to other architectures.

Limitation

The RISE framework requires a large amount of computational resources and may not be feasible for all applications.

Expert Commentary

This study is a significant contribution to the field of natural language processing and language modeling. The authors have provided a comprehensive analysis of expert routing patterns in MoE models, revealing a previously unknown phenomenon. The proposed RISE framework is a practical and effective solution for improving low-resource language performance. However, the study is limited to a specific type of MoE model and may not generalize to other architectures. Additionally, the RISE framework requires a large amount of computational resources and may not be feasible for all applications. Nevertheless, the study has important implications for both practical applications and policy-making.

Recommendations

  • ✓ Future research should focus on generalizing the findings of this study to other types of MoE models and architectures.
  • ✓ Developers and researchers should invest in computational resources and infrastructure to enable the wide adoption of the RISE framework.

Sources

Original: arXiv - cs.CL

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