Title: Quantifying Expert Specialization for Effective Pruning in Mixture-of-Experts Models
Primary area: foundation or frontier models, including LLMs
Keywords: Mixture-of-Experts, Expert Pruning, Fine-Tuning Free

Abstract: Mixture-of-Experts (MoE) architectures enable efficient scaling of language models through sparse activation. However, their deployment is hindered by a significant memory bottleneck, as all expert parameters must remain resident in memory. Expert pruning is an effective technique to mitigate this issue. Existing methods rely on layer-wise metrics based on either routing behavior or expert outputs. These approaches fail to capture the global influence of an expert on cross-layer information flow. In this paper, we introduce a framework for cross-layer information flow analysis. We propose a novel metric called the Expert Specialization Index (ESI). ESI quantifies the entropy of an expert's influence on downstream routing distributions. This allows it to distinguish between functionally specialized experts and redundant, general-purpose ones. Our analysis on Mixtral-8x7B and Qwen1.5-MoE reveals significant differences in their expert specialization profiles. This leads to a key finding we term architecture-strategy fit. Models with highly specialized experts benefit from preserving the original routing distribution via redirection. In contrast, models with less specialized experts are better served by removing experts and re-normalizing routing probabilities. Supported by experimental results, our ESI analysis allows us to explore how to design compression strategies for different MoE architectures. Our findings provide insights into the relationship between model architecture and effective compression strategies.