arXiv AI
Unveiling Language Routing Isolation in Multilingual MoE Models for Interpretable Subnetwork Adaptation
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arXiv AI
Oct 1
RA-MoE: Routing-Aligned Fine-Tuning for Multilingual Adaptation of Mixture-of-Experts Models
arXiv:2605.28306v2 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) models enable efficient LLM scaling, yet adapting them to non-English downstream tasks remains challenging. Standard...
arXiv Computation and Language
Aug 31
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.
Hugging Face Trending Papers
Aug 15
A Declarative-Procedural Perspective on Expert Routing in Bilingual Mixture-of-Experts Language Models
We investigate whether Mixture-of-Experts (MoE) language models develop linguistically structured expert routing during bilingual language acquisition. Inspired by the Declarative-Procedural framework...
arXiv Computation and Language
Aug 24
RARE: Decoupling Representation Steering from Expert Routing in Mixture-of-Experts Language Models
The paper introduces RARE, a router‑agnostic representation engineering framework for Mixture‑of‑Experts language models. RARE projects behavioral perturbations onto the null space of the router matrix to avoid affecting routing, and corrects downstream routing drift. Experiments on six open‑weight MoE models show that RARE improves steering tasks—reducing harmfulness, increasing truthfulness, and enhancing factual editing—while preserving overall model accuracy.
arXiv AI
Sep 17
MoRE: Mixture of Reused Experts
MoRE: Mixture of Reused Experts is a hybrid architecture that combines Mixture-of-Experts (MoE) with weight‑sharing techniques. It shares expert pools across adjacent layers while each layer keeps its own router, and introduces lightweight depth embeddings to help shared experts differentiate layer contexts. Experiments on models ranging from 114 M to 1.15 B parameters show MoRE achieves lower perplexity and better downstream performance than standard MoEs and other weight‑sharing models, with only minimal changes to existing MoE implementations.
arXiv AI
Sep 15
Task-Aware Federated Fine-Tuning for MoE-based Large Language Models
The paper introduces FedTAR, a task-aware federated fine‑tuning approach for Mixture‑of‑Experts (MoE) large language models. FedTAR links local client updates to task preferences using routing outputs and Singular Value Decomposition to extract low‑dimensional task coordinates and update directions. It then aggregates updates within and across task clusters, reconstructing the final update to preserve expert specialization and reduce interference, achieving state‑of‑the‑art performance on four benchmark tasks under non‑IID settings.