The Expert Strikes Back: Interpreting Mixture-of-Experts Language Models at Expert Level
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...
| Authors | Jeremy Herbst, Stefan Wermter, Jae Hee Lee |
|---|---|
| Primary category | cs.CL |
| Published | 2 April 2026 |
| Last updated at source | 15 May 2026 |
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