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

Jun Bai; Minghao Tong; Yang Liu; Zixia Jia; Zilong Zheng

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arXiv · 2508.19594

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

Jun Bai·Minghao Tong·Yang Liu·Zixia Jia·Zilong Zheng

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Abstract

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.

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Jun Bai, Minghao Tong, Yang Liu, Zixia Jia, Zilong Zheng. 2025-08-27. Understanding and Leveraging the Expert Specialization of Context Faithfulness in Mixture-of-Experts LLMs. https://arxiv.org/abs/2508.19594

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