大语言模型中的专家混合架构
Mixture of Experts in Large Language Models
AI总结：
本文研究了大语言模型中专家混合架构的性能提升与应用挑战，分析了其核心机制与优化方法，探讨了MoE在任务特定性能和模型扩展方面的优势。
摘要：
本文全面回顾了大语言模型中专家混合（MoE）架构，突显其显著提升模型性能同时保持最小计算开销的能力。通过系统分析涵盖理论基础、核心架构设计和大语言模型（LLM）应用，我们探讨了专家门控和路由机制、分层和稀疏MoE配置、元学习方法、多模态和多任务学习场景、实际部署案例以及深度学习中的最新进展和挑战。我们的分析指出MoE的关键优势，包括比等效贝叶斯方法更优越的模型容量、改进的任务特定性能以及高效扩展模型容量的能力。我们还强调确保专家多样性、准确校准和可靠推断聚合的重要性，这些是最大化MoE架构效果的关键。最后，本文概述了当前研究局限性、开放挑战和有前途的未来方向，为MoE架构及其应用的持续创新提供了基础。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
