Routing in Sparsely-gated Language Models responds to Context

Stefan Arnold, Marian Fietta, Dilara Yesilbas

Track: Full paper

Keywords: Language Models, Mixture-of-Experts, Routing

TL;DR: Contradicting prior research, we find evidence that routing in sparsely-gated langauge models is context sensitive.

Abstract: Language Models (LMs) recently incorporate mixture-of-experts layers consisting of a router and a collection of experts to scale up their parameter count given a fixed computational budget. Building on previous efforts indicating that token-expert assignments are predominantly influenced by token identities and positions, we trace routing decisions of similarity-annotated text pairs to evaluate the context sensitivity of learned token-expert assignments. We observe that routing in encoder layers mainly depends on (semantic) associations, but contextual cues provide an additional layer of refinement. Conversely, routing in decoder layers is more variable and markedly less sensitive to context.

Submission Number: 4