Mixture of Experts in Large Language Models

TL;DR

This paper reviews the Mixture-of-Experts architecture in large language models, emphasizing its ability to improve performance and scalability while discussing design, challenges, and future research directions.

Contribution

It provides a comprehensive analysis of MoE architectures, covering theoretical foundations, design choices, applications, and recent advances, highlighting key challenges and future directions.

Findings

MoE enhances model capacity with minimal computational overhead.

Expert diversity and calibration are crucial for effective MoE performance.

Recent advances address scalability and deployment challenges.

Abstract

This paper presents a comprehensive review of the Mixture-of-Experts (MoE) architecture in large language models, highlighting its ability to significantly enhance model performance while maintaining minimal computational overhead. Through a systematic analysis spanning theoretical foundations, core architectural designs, and large language model (LLM) applications, we examine expert gating and routing mechanisms, hierarchical and sparse MoE configurations, meta-learning approaches, multimodal and multitask learning scenarios, real-world deployment cases, and recent advances and challenges in deep learning. Our analysis identifies key advantages of MoE, including superior model capacity compared to equivalent Bayesian approaches, improved task-specific performance, and the ability to scale model capacity efficiently. We also underscore the importance of ensuring expert diversity, accurate calibration, and reliable inference aggregation, as these are essential for maximizing the effectiveness of MoE architectures. Finally, this review outlines current research limitations, open challenges, and promising future directions, providing a foundation for continued innovation in MoE architecture and its applications.