  Sparse mixture of experts (SMoE) have emerged as an effective approach for
scaling large language models while keeping a constant computational cost.
Regardless of several notable successes of SMoE, effective training such
architecture remains elusive due to the representation collapse problem, which
in turn harms model performance and causes parameter redundancy. In this work,
we present Similarity-based Sparse Mixture of Experts (SimSMoE), a novel
similarity of neural network algorithm, that guarantees a solution to address
the representation collapse issue between experts given a fixed FLOPs budget.
We conduct extensive empirical evaluations on three large language models for
both Pre-training and Fine-tuning tasks to illustrate the efficacy, robustness,
and scalability of our method. The results demonstrate that SimSMoE
significantly enhances existing routing policy and outperforms other SMoE
training methods in performance for the tasks.
