mlsanityData to 14 Oct 2026

Understanding and Leveraging the Expert Specialization of Context Faithfulness in Mixture-of-Experts LLMs

arXiv 2508.19594 ↗First posted 27 Aug 2025cs.CLcited by 12

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

Abstract (arXiv)

Context faithfulness is essential for reliable reasoning in context-dependent scenarios. However, large language models often struggle to ground their outputs in the provided context, resulting in irrelevant responses. Inspired by the emergent expert specialization observed in mixture-of-experts architectures, this work investigates whether certain experts exhibit specialization in context utilization, offering a potential pathway toward targeted optimization for improved context faithfulness. To explore this, we propose Router Lens, a method that accurately identifies context-faithful experts. Our analysis reveals that these experts progressively amplify attention to relevant contextual information, thereby enhancing context grounding. Building on this insight, we introduce Context-faithful Expert Fine-Tuning (CEFT), a lightweight optimization approach that selectively fine-tunes context-faithful experts. Experiments across a wide range of benchmarks and models demonstrate that CEFT matches or surpasses the performance of full fine-tuning while being significantly more efficient.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Placed in Expert routing strategies, one of 12 directions in Mixture-of-experts. Builds on 32 of its 48 references that are in the map. First posted on arXiv 27 Aug 2025.

Builds on

32 of 48 references.

Closest ancestors · 3

Same direction, newest first

  1. 2024Part-Of-Speech Sensitivity of Routers in Mixture of Experts Models22 Dec
  2. 2024Auxiliary-Loss-Free Load Balancing Strategy for Mixture-of-Experts28 Aug
  3. 2022StableMoE: Stable Routing Strategy for Mixture of Experts18 Apr

Other references · 29

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Measuring Massive Multitask Language Understandingcited by 7,963
  3. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,148
  4. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,754
  5. 2022Locating and Editing Factual Associations in GPTcited by 2,691
  6. 2024DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Modelscited by 867
  7. 2015A Neural Attention Model for Abstractive Sentence Summarizationcited by 396
  8. 2021Entity-Based Knowledge Conflicts in Question Answeringcited by 356
  9. 2023Adaptive Chameleon or Stubborn Sloth: Revealing the Behavior of Large Language Models in Knowledge Conflictscited by 341
  10. 2023Trusting Your Evidence: Hallucinate Less with Context-aware Decodingcited by 327
  11. 2024Knowledge Conflicts for LLMs: A Surveycited by 283
  12. 2024RAFT: Adapting Language Model to Domain Specific RAGcited by 262
17 more
  1. 2023LooGLE: Can Long-Context Language Models Understand Long Contexts?cited by 202
  2. 2024OpenMoE: An Early Effort on Open Mixture-of-Experts Language Modelscited by 187
  3. 2024Lookback Lens: Detecting and Mitigating Contextual Hallucinations in Large Language Models Using Only Attention Mapscited by 142
  4. 2024ReDeEP: Detecting Hallucination in Retrieval-Augmented Generation via Mechanistic Interpretabilitycited by 101
  5. 2024ContextCite: Attributing Model Generation to Contextcited by 98
  6. 2024Trustworthiness in Retrieval-Augmented Generation Systems: A Surveycited by 83
  7. 2024Context-DPO: Aligning Language Models for Context-Faithfulnesscited by 50
  8. 2024Competition of Mechanisms: Tracing How Language Models Handle Facts and Counterfactualscited by 40
  9. 2024Your Mixture-of-Experts LLM Is Secretly an Embedding Model For Freecited by 37
  10. 2024In-Context Editing: Learning Knowledge from Self-Induced Distributionscited by 34
  11. 2024Context versus Prior Knowledge in Language Modelscited by 26
  12. 2024Controllable Context Sensitivity and the Knob Behind Itcited by 23
  13. 2025ReflectEvo: Improving Meta Introspection of Small LLMs by Learning Self-Reflectioncited by 12
  14. 2024Are the Values of LLMs Structurally Aligned with Humans? A Causal Perspectivecited by 9
  15. 2025RuleReasoner: Reinforced Rule-based Reasoning via Domain-aware Dynamic Samplingcited by 6
  16. 2025TokenSwift: Lossless Acceleration of Ultra Long Sequence Generationcited by 4
  17. 2025Selecting Demonstrations for Many-Shot In-Context Learning via Gradient Matchingcited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 9.

12 papers.

  1. 2025Native Parallel Reasoner: Reasoning in Parallelism via Self-Distilled Reinforcement Learningcited by 12
  2. 2026The Illusion of Specialization: Unveiling the Domain-Invariant "Standing Committee" in Mixture-of-Experts Modelscited by 6
  3. 2025AsyMoE: Leveraging Modal Asymmetry for Enhanced Expert Specialization in Large Vision-Language Modelscited by 3
  4. 2025Beyond Black-Box Interventions: Latent Probing for Faithful Retrieval-Augmented Generationcited by 2
  5. 2026LM-Lexicon: Improving Definition Modeling via Harmonizing Semantic Expertscited by 0
  6. 2026A Closer Look into LLMs for Table Understandingcited by 0
  7. 2026DBES: A Systematic Benchmark and Metric Suite for Evaluating Expert Specialization in Large-Scale MoEscited by 0
  8. 2026Hyperbolic and Evidence-Prioritized Experts for Large Vision-Language Modelscited by 0
  9. 2026TEXAS: Task-Expert-Aware Supervision for Downstream Mixture-of-Experts LLM Adaptationcited by 0
  10. 2026Router Prior Bias: Preserving Base Routing Structure in MoE Post-Trainingcited by 0
  11. 2026Task-Aware Federated Fine-Tuning for MoE-based Large Language Modelscited by 0
  12. 2026From Routing Signals to Selective Review: Visual regrounding in MoE VLMscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Multi-level context Modeling for consistent expert selection in Mixture-of-Experts
  2. 2026You Only Need 2/3 of the Chosen Experts: An Empirical Study of Dynamic Expert Pruning in Fine-Grained MoE LLMs
  3. 2025Dynamic Experts Search: Enhancing Reasoning in Mixture-of-Experts LLMs at Test Time
  4. 2025How Many Experts Are Enough? Towards Optimal Semantic Specialization for Mixture-of-Experts
  5. 2026It Takes a MAESTRO To Prune Bad Experts
  6. 2026The Myth of Expert Specialization in MoEs: Why Routing Reflects Geometry, Not Necessarily Domain Expertise
  7. 2026Does Role Specialization Matter for Explanation Faithfulness in Mixture-of-Experts?
  8. 2026Slicing and Dicing: Configuring Optimal Mixtures of Experts