Mixture of Expert Architecture

Mixture-of-Experts (MoE) architectures aim to improve the efficiency and adaptability of large language models by distributing computation across specialized expert networks. Current research focuses on enhancing routing mechanisms for efficient expert selection, developing methods for adding or adapting experts to new tasks with minimal retraining, and exploring the application of MoEs in diverse areas like summarization, robotics, and secure machine learning. This approach offers a parameter-efficient alternative to simply scaling up model size, leading to improved performance and resource utilization in various applications.