A Declarative-Procedural Perspective on Expert Routing in Bilingual Mixture-of-Experts Language Models

• URL: http://arxiv.org/abs/2608.15102v1
• Date: Sat, 15 Aug 2026 07:53:12 GMT
• Title: A Declarative-Procedural Perspective on Expert Routing in Bilingual Mixture-of-Experts Language Models
• Abstract summary: We investigate whether Mixture-of-Experts (MoE) language models develop linguistically structured expert routing during bilingual language acquisition.<n>Inspired by the Declarative-Procedural framework, we analyze lexical, grammatical, and syntactic processing in a decoder-only English-German MoE Transformer trained under sequential language exposure.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We investigate whether Mixture-of-Experts (MoE) language models develop linguistically structured expert routing during bilingual language acquisition. Inspired by the Declarative-Procedural framework, we analyze lexical, grammatical, and syntactic processing in a decoder-only English-German MoE Transformer trained under sequential language exposure. We construct a probe-based validation set and extract token-level routing distributions to quantify category-dependent specialisation using mutual information, routing entropy, and Jensen-Shannon distance. The curriculum-trained model exhibits a peak mutual information of 0.1148 at layer 5, indicating category-dependent differences in routing distributions across linguistic categories. Surprisingly, a no-curriculum baseline trained on mixed English-German data shows stronger aggregate specialisation, reaching a peak mutual information of 0.2599 at the same layer. These results suggest that interpretable linguistic organization emerges within MoE routing patterns even without sequential language exposure. A replication at a second training seed shows that the no-curriculum condition's specialisation concentrates on a single language whose identity is seed-dependent, whereas the curriculum consistently yields a stable, language-balanced routing profile; rather than uniformly increasing specialisation, staged bilingual exposure reduces single-language dominance. The official Github repository: https://github.com/Amrit828/DP-Theory-MOE-Interpretability-Research

Related papers

• Leveraging Routing Dynamics in Mixture-of-Experts Models for Efficient Language Adaptation [50.731838216268706]
  We study multilingual routing dynamics during continual pre-training of an English-centric MoE model on a multilingual corpus.<n>We find that continual multilingual pre-training leads to diffused, language-agnostic routing in early and middle layers.<n>We propose a parameter-efficient adaptation strategy that updates language-specific and shared experts in the final MoE layers.
  arXiv  Detail & Related papers  (2026-05-28T10:12:41Z)
• Unveiling Language Routing Isolation in Multilingual MoE Models for Interpretable Subnetwork Adaptation [65.69228479949693]
  Mixture-of-Experts (MoE) models exhibit striking performance disparities across languages.<n>We propose RISE (Routing Isolation-guided Subnetwork Enhancement), a framework that exploits routing isolation to identify and adapt language-specific expertworks.
  arXiv  Detail & Related papers  (2026-04-04T04:56:35Z)
• Adapting Language Models to Indonesian Local Languages: An Empirical Study of Language Transferability on Zero-Shot Settings [1.1556013985948772]
  We evaluate transferability of pre-trained language models to low-resource Indonesian local languages.<n>We group the target languages into three categories: seen, partially seen, and unseen.<n> Multilingual models perform best on seen languages, moderately on partially seen ones, and poorly on unseen languages.<n>We find that MAD-X significantly improves performance, especially for seen and partially seen languages, without requiring labeled data in the target language.
  arXiv  Detail & Related papers  (2025-07-02T12:17:55Z)
• Improving In-context Learning of Multilingual Generative Language Models with Cross-lingual Alignment [42.624862172666624]
  We propose a simple yet effective cross-lingual alignment framework exploiting pairs of translation sentences. It aligns the internal sentence representations across different languages via multilingual contrastive learning. Experimental results show that even with less than 0.1 textperthousand of pre-training tokens, our alignment framework significantly boosts the cross-lingual abilities of generative language models.
  arXiv  Detail & Related papers  (2023-11-14T11:24:08Z)
• Bridging Cross-Lingual Gaps During Leveraging the Multilingual Sequence-to-Sequence Pretraining for Text Generation [80.16548523140025]
  We extend the vanilla pretrain-finetune pipeline with extra code-switching restore task to bridge the gap between the pretrain and finetune stages. Our approach could narrow the cross-lingual sentence representation distance and improve low-frequency word translation with trivial computational cost.
  arXiv  Detail & Related papers  (2022-04-16T16:08:38Z)
• On Cross-Lingual Retrieval with Multilingual Text Encoders [51.60862829942932]
  We study the suitability of state-of-the-art multilingual encoders for cross-lingual document and sentence retrieval tasks. We benchmark their performance in unsupervised ad-hoc sentence- and document-level CLIR experiments. We evaluate multilingual encoders fine-tuned in a supervised fashion (i.e., we learn to rank) on English relevance data in a series of zero-shot language and domain transfer CLIR experiments.
  arXiv  Detail & Related papers  (2021-12-21T08:10:27Z)
• A Massively Multilingual Analysis of Cross-linguality in Shared Embedding Space [61.18554842370824]
  In cross-lingual language models, representations for many different languages live in the same space. We compute a task-based measure of cross-lingual alignment in the form of bitext retrieval performance. We examine a range of linguistic, quasi-linguistic, and training-related features as potential predictors of these alignment metrics.
  arXiv  Detail & Related papers  (2021-09-13T21:05:37Z)
• VECO: Variable and Flexible Cross-lingual Pre-training for Language Understanding and Generation [77.82373082024934]
  We plug a cross-attention module into the Transformer encoder to explicitly build the interdependence between languages. It can effectively avoid the degeneration of predicting masked words only conditioned on the context in its own language. The proposed cross-lingual model delivers new state-of-the-art results on various cross-lingual understanding tasks of the XTREME benchmark.
  arXiv  Detail & Related papers  (2020-10-30T03:41:38Z)
• Cross-lingual Spoken Language Understanding with Regularized Representation Alignment [71.53159402053392]
  We propose a regularization approach to align word-level and sentence-level representations across languages without any external resource. Experiments on the cross-lingual spoken language understanding task show that our model outperforms current state-of-the-art methods in both few-shot and zero-shot scenarios.
  arXiv  Detail & Related papers  (2020-09-30T08:56:53Z)
• A Deep Reinforced Model for Zero-Shot Cross-Lingual Summarization with Bilingual Semantic Similarity Rewards [40.17497211507507]
  Cross-lingual text summarization is a practically important but under-explored task. We propose an end-to-end cross-lingual text summarization model.
  arXiv  Detail & Related papers  (2020-06-27T21:51:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.