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

• URL: http://arxiv.org/abs/2508.19594v2
• Date: Tue, 16 Sep 2025 08:17:06 GMT
• Title: Understanding and Leveraging the Expert Specialization of Context Faithfulness in Mixture-of-Experts LLMs
• Authors: Jun Bai, Minghao Tong, Yang Liu, Zixia Jia, Zilong Zheng,
• Abstract summary: This work investigates whether certain experts exhibit specialization in context utilization.<n>We introduce Context-faithful Expert Fine-Tuning (CEFT), a lightweight optimization approach that selectively fine-tunes context-faithful experts.
• Score: 33.32130125757288
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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