Unveiling Language Routing Isolation in Multilingual MoE Models for Interpretable Subnetwork Adaptation
Summary: arXiv:2604.03592v1 Announce Type: cross
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.
Introduction
The advancements in multilingual models have significantly improved the capabilities of natural language processing systems. However, the performance of these models varies greatly across different languages. Understanding the underlying mechanisms that contribute to these performance disparities is crucial for developing more effective multilingual systems.
Key Findings
This research uncovers the phenomenon of Language Routing Isolation within MoE models. Key findings include:
- High-resource languages and low-resource languages activate largely disjoint sets of experts.
- Routing patterns exhibit a distinct pattern of convergence and divergence across the depth of the model.
- By analyzing these routing patterns, we can enhance language-specific performance through targeted adaptations.
RISE Framework
The proposed RISE framework leverages the insights gained from the analysis of routing patterns. It employs a tripartite selection strategy that includes:
- Specificity Scores: These scores identify language-specific experts in both shallow and deep layers of the model.
- Overlap Scores: These scores help in selecting universal experts that can benefit multiple languages, particularly in the middle layers.
- Subnetwork Training: By training only the selected subnetworks and freezing the other parameters, RISE significantly boosts performance in low-resource languages.
Experimental Results
Experiments conducted on a diverse set of 10 languages demonstrate the effectiveness of the RISE framework. The results indicate:
- Target-language F1 score improvements of up to 10.85%.
- Minimal degradation in performance for other languages, showcasing the adaptability of the model.
Conclusion
The findings of this study reveal that understanding expert routing patterns in MoE models is essential for improving multilingual capabilities. The RISE framework not only enhances performance for low-resource languages but also preserves the overall efficiency of the model. This work sets the stage for future research into language-specific adaptations in multilingual settings.
By implementing RISE and similar frameworks, developers can create more effective and interpretable multilingual systems, ultimately benefiting a wider range of languages and applications.