arXiv AI
RA-MoE: Routing-Aligned Fine-Tuning for Multilingual Adaptation of Mixture-of-Experts Models
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Unveiling Language Routing Isolation in Multilingual MoE Models for Interpretable Subnetwork Adaptation
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A Declarative-Procedural Perspective on Expert Routing in Bilingual Mixture-of-Experts Language Models
The study examines whether Mixture-of-Experts (MoE) language models develop linguistically structured expert routing during bilingual language acquisition. Using a decoder-only English‑German MoE Transformer trained with sequential language exposure, the authors probe token‑level routing distributions and measure category‑dependent specialization via mutual information, routing entropy, and Jensen‑Shannon distance. Results show that a curriculum‑trained model peaks at a mutual information of 0.1148 at layer 5, while a no‑curriculum baseline trained on mixed data achieves a higher peak of 0.2599 at the same layer, indicating stronger aggregate specialization. Replication with a second training seed reveals that the no‑curriculum condition’s specialization focuses on a single language in a seed‑dependent way, whereas the curriculum consistently yields a stable, language‑balanced routing profile, suggesting that staged bilingual exposure reduces single‑language dominance.
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A Declarative-Procedural Perspective on Expert Routing in Bilingual Mixture-of-Experts Language Models
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TEXAS: Task-Expert-Aware Supervision for Downstream Mixture-of-Experts LLM Adaptation
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arXiv AI
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Task-Aware Federated Fine-Tuning for MoE-based Large Language Models
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