mlsanityData to 14 Oct 2026

Understanding Multilingualism in Mixture-of-Experts LLMs: Routing Mechanism, Expert Specialization, and Layerwise Steering

arXiv 2601.14050 ↗First posted 20 Jan 2026cs.CLcited by 10

On the map: Mixture-of-experts → Expert routing strategies

Abstract (arXiv)

Mixture-of-Experts (MoE) architectures have shown strong multilingual capabilities, yet the internal mechanisms underlying performance gains and cross-language differences remain insufficiently understood. In this work, we conduct a systematic analysis of MoE models, examining routing behavior and expert specialization across languages and network depth. Our analysis reveals that multilingual processing in MoE models is highly structured: routing aligns with linguistic families, expert utilization follows a clear layerwise pattern, and high-resource languages rely on shared experts while low-resource languages depend more on language-exclusive experts despite weaker performance. Layerwise interventions further show that early and late MoE layers support language-specific processing, whereas middle layers serve as language-agnostic capacity hubs. Building on these insights, we propose a routing-guided steering method that adaptively guides routing behavior in middle layers toward shared experts associated with dominant languages at inference time, leading to consistent multilingual performance improvements, particularly for linguistically related language pairs. Our code is available at https://github.com/conctsai/Multilingualism-in-Mixture-of-Experts-LLMs.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Placed in Expert routing strategies, one of 12 directions in Mixture-of-experts. Builds on 33 of its 45 references that are in the map. First posted on arXiv 20 Jan 2026.

Builds on

33 of 45 references.

Closest ancestors · 1

Same direction, newest first

  1. 2025Multilingual Routing in Mixture-of-Experts6 Oct

Other references · 32

  1. 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,509
  2. 2024DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Modelscited by 867
  3. 2019Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Prunedcited by 856
  4. 2019On the Cross-lingual Transferability of Monolingual Representationscited by 578
  5. 2022Language Models are Multilingual Chain-of-Thought Reasonerscited by 530
  6. 2023The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variantscited by 311
  7. 2024Do Llamas Work in English? On the Latent Language of Multilingual Transformerscited by 281
  8. 2024How do Large Language Models Handle Multilingualism?cited by 150
  9. 2023Cross-lingual Prompting: Improving Zero-shot Chain-of-Thought Reasoning across Languagescited by 97
  10. 2024On the Multilingual Ability of Decoder-based Pre-trained Language Models: Finding and Controlling Language-Specific Neuronscited by 83
  11. 2025Do Multilingual LLMs Think In English?cited by 78
  12. 2024The Semantic Hub Hypothesis: Language Models Share Semantic Representations Across Languages and Modalitiescited by 69
20 more
  1. 2025Large Language Models Share Representations of Latent Grammatical Concepts Across Typologically Diverse Languagescited by 59
  2. 2025PolyMath: Evaluating Mathematical Reasoning in Multilingual Contextscited by 56
  3. 2025Crosslingual Reasoning through Test-Time Scalingcited by 46
  4. 2025Understand, Solve and Translate: Bridging the Multilingual Mathematical Reasoning Gapcited by 43
  5. 2025Linguistic Generalizability of Test-Time Scaling in Mathematical Reasoningcited by 37
  6. 2024Enhancing Multilingual Capabilities of Large Language Models through Self-Distillation from Resource-Rich Languagescited by 36
  7. 2024SeaLLMs 3: Open Foundation and Chat Multilingual Large Language Models for Southeast Asian Languagescited by 36
  8. 2024Sharing Matters: Analysing Neurons Across Languages and Tasks in LLMscited by 33
  9. 2025The Emergence of Abstract Thought in Large Language Models Beyond Any Languagecited by 21
  10. 2024AutoCAP: Towards Automatic Cross-lingual Alignment Planning for Zero-shot Chain-of-Thoughtcited by 19
  11. 2025SLAM: Towards Efficient Multilingual Reasoning via Selective Language Alignmentcited by 18
  12. 2021Contributions of Transformer Attention Heads in Multi- and Cross-lingual Taskscited by 16
  13. 2025Babel: Open Multilingual Large Language Models Serving Over 90% of Global Speakerscited by 16
  14. 2025Could Thinking Multilingually Empower LLM Reasoning?cited by 16
  15. 2025Enhancing Non-English Capabilities of English-Centric Large Language Models through Deep Supervision Fine-Tuningcited by 11
  16. 2025LayAlign: Enhancing Multilingual Reasoning in Large Language Models via Layer-Wise Adaptive Fusion and Alignment Strategycited by 10
  17. 2025Disparities in LLM Reasoning Accuracy and Explanations: A Case Study on African American Englishcited by 8
  18. 2025A Post-trainer's Guide to Multilingual Training Data: Uncovering Cross-lingual Transfer Dynamicscited by 6
  19. 2024Why We Build Local Large Language Models: An Observational Analysis from 35 Japanese and Multilingual LLMscited by 4
  20. 2024Probing self-attention in self-supervised speech models for cross-linguistic differencescited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 10.

10 papers.

  1. 2026The Myth of Expert Specialization in MoEs: Why Routing Reflects Geometry, Not Necessarily Domain Expertisecited by 13
  2. 2026AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learningcited by 12
  3. 2026Unveiling Language Routing Isolation in Multilingual MoE Models for Interpretable Subnetwork Adaptationcited by 4
  4. 2026RASA: Routing-Aware Safety Alignment for Mixture-of-Experts Modelscited by 3
  5. 2026DiMtextsuperscript3: Bridging Multilingual and Multimodal Models via Direction- and Magnitude-Aware Mergingcited by 1
  6. 2026Code-Switching Reveals Language Anchoring in Multilingual LLMscited by 1
  7. 2026Routing-Aligned Fine-Tuning for Multilingual Downstream Tasks in Mixture-of-Experts Modelscited by 0
  8. 2026Is MoE Routing a Huffman Code? Discovering the Frequency-Diversity Law in Chain-of-Thoughtcited by 0
  9. 2026Travel Back To Move Forward: Retrospective Exploration and Evolving for Long Horizon Agentscited by 0
  10. 2026Overcoming Data Imbalances with Expert Selection Biasescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Leveraging Routing Dynamics in Mixture-of-Experts Models for Efficient Language Adaptation
  2. 2026SARA: Unlocking Multilingual Knowledge in Mixture-of-Experts via Semantically Anchored Routing Alignment
  3. 2026Mixture of Experts for Low-Resource LLMs
  4. 2026A Declarative-Procedural Perspective on Expert Routing in Bilingual Mixture-of-Experts Language Models
  5. 2024LIBMoE: A Library for comprehensive benchmarking Mixture of Experts in Large Language Models
  6. 2025Advancing Expert Specialization for Better MoE
  7. 2026Slicing and Dicing: Configuring Optimal Mixtures of Experts
  8. 2024A Closer Look into Mixture-of-Experts in Large Language Models