MoE language models are naturally more interpretable than dense models because their sparse architecture pushes experts toward single, well-defined tasks like 'closing LaTeX brackets' rather than handling multiple unrelated functions.
This paper investigates how Mixture-of-Experts language models work by analyzing individual experts instead of neurons. The researchers find that MoE experts are less ambiguous (monosemantic) than dense networks, and that experts specialize in specific linguistic tasks rather than broad domains—making MoE models easier to understand and interpret at scale.