Part-Of-Speech Sensitivity of Routers in Mixture of Experts Models
This provides insights into linguistic processing in MoE models for NLP researchers, but it is incremental as it analyzes existing models without proposing new methods.
The study investigated how Mixture of Experts (MoE) models route tokens based on Part-of-Speech (POS) tags, finding that experts specialize in specific POS categories with routing paths showing high predictive accuracy for POS.
This study investigates the behavior of model-integrated routers in Mixture of Experts (MoE) models, focusing on how tokens are routed based on their linguistic features, specifically Part-of-Speech (POS) tags. The goal is to explore across different MoE architectures whether experts specialize in processing tokens with similar linguistic traits. By analyzing token trajectories across experts and layers, we aim to uncover how MoE models handle linguistic information. Findings from six popular MoE models reveal expert specialization for specific POS categories, with routing paths showing high predictive accuracy for POS, highlighting the value of routing paths in characterizing tokens.