A theoretical model for task routing in mixture-of-expert transformers
This work offers a theoretical foundation for empirical observations of localized knowledge in MoE transformers, benefiting researchers studying model interpretability and scaling.
The paper provides a theoretical explanation for task-expert specialization in mixture-of-experts (MoE) transformers using discrete models of language, proving that a single-layer MoE transformer can encode knowledge by routing queries to task-specific experts whose size depends on task complexity.
Mixture-of-experts (MoE) layers enable the scaling of transformer models while keeping the inference compute fixed. While task-expert specialization has been observed in empirical studies of frontier MoE transformer models, existing theoretical work analyzes this using continuous mixture models that cannot be used to model natural language effectively. An important open question is to \textit{theoretically explain task-expert specialization in transformer MoE models using discrete models of language}. To address this, we represent structured knowledge via syntactic templates and finite key-value dictionaries, and prove formally that a single-layer MoE transformer can encode knowledge by using experts that specialize in the corresponding tasks. Our construction shows how queries are routed to unique, task-specific experts whose size depends solely on the intrinsic complexity of the given task (i.e. the combined size of its syntactic templates and factual dictionary). Our construction provides a theoretical support for empirical results on localized knowledge circuits in MoE models. We support our theoretical findings with experiments evaluating model performance under varying MoE loss functions.