The Expert Strikes Back: Interpreting Mixture-of-Experts Language Models at Expert Level

TL;DR

This paper demonstrates that Mixture-of-Experts models are inherently more interpretable at the expert level due to their monosemantic neurons and experts, facilitating better understanding of their linguistic and semantic functions.

Contribution

It introduces a novel expert-level analysis approach showing MoE experts are specialized in linguistic and semantic tasks, improving interpretability of large language models.

Findings

MoE experts are less polysemantic than dense neurons.

Sparsity encourages monosemanticity in neurons and experts.

Experts function as fine-grained task specialists rather than broad domain experts.

Abstract

Mixture-of-Experts (MoE) architectures have become the dominant choice for scaling Large Language Models (LLMs), activating only a subset of parameters per token. While MoE architectures are primarily adopted for computational efficiency, it remains an open question whether their sparsity makes them inherently easier to interpret than dense feed-forward networks (FFNs). We compare MoE experts and dense FFNs using $k$-sparse probing and find that expert neurons are consistently less polysemantic, with the gap widening as routing becomes sparser. This suggests that sparsity pressures both individual neurons and entire experts toward monosemanticity. Leveraging this finding, we zoom out from the neuron to the expert level as a more effective unit of analysis. We validate this approach by automatically interpreting hundreds of experts. This analysis allows us to resolve the debate on specialization: experts are neither broad domain specialists (e.g., biology) nor simple token-level processors. Instead, they function as fine-grained task experts, specializing in linguistic operations or semantic tasks (e.g., closing brackets in $\LaTeX{}$). Our findings suggest that MoEs are inherently interpretable at the expert level, providing a clearer path toward large-scale model interpretability. Code is available at: https://github.com/jerryy33/MoE_analysis.