Key Points
1. Large language models (LLMs) have made unprecedented advancements across diverse fields, enabled by their substantial model size, extensive datasets, and vast computational power.
2. Mixture of experts (MoE) has emerged as an effective method for scaling up model capacity with minimal computational overhead, gaining significant attention.
3. This survey aims to provide a systematic and comprehensive review of the literature on MoE, serving as an essential resource for researchers.
4. The survey proposes a new taxonomy to categorize MoE advancements into algorithm design, system design, and applications.
5. For algorithm design, the survey covers the prevalent gating functions, expert network architectures, and recent innovations in MoE derivatives.
6. For system design, the survey examines the challenges and solutions related to computation, communication, and storage in distributed MoE systems.
7. The survey covers applications of MoE models in natural language processing, computer vision, recommender systems, and multimodal domains.
8. The survey identifies critical challenges and opportunities in MoE research, including training stability, scalability, expert specialization, and interpretability.
9. To facilitate ongoing updates and knowledge sharing, the authors have established a dedicated resource repository for the latest developments in MoE research.
Summary
The key points from the research paper on large language models (LLMs) and the mixture of experts (MoE) approach are as follows:
The paper first provides an overview of the advancements in LLMs, which have achieved remarkable capabilities across diverse fields. This is attributed to their substantial model size, extensive datasets, and vast computational power during training. Within this context, the mixture of experts (MoE) has emerged as an effective method for significantly scaling up model capacity with minimal computational overhead.
The paper then proposes a new taxonomy for categorizing MoE advancements. This taxonomy covers three main aspects: algorithm design, system design, and applications.
In the algorithm design section, the paper delves into the structure of the MoE layer, including the prevalent sparse and dense activation of experts, as well as the emerging soft activation methods like token merging and expert merging. It also reviews the expert network types, such as feed-forward networks, attention, and others, along with the key hyperparameters like expert count, expert size, and placement frequency of MoE layers.
The system design section focuses on the computational, communication, and storage challenges introduced by MoE models. It discusses strategies to enhance computational efficiency, optimize communication overhead, and manage storage constraints, drawing insights from various open-source MoE system frameworks.
Finally, the paper explores the diverse applications of MoE models, including natural language processing, computer vision, recommender systems, and multimodal tasks. It highlights how MoE architectures have enabled significant performance improvements and expanded capabilities in these domains.
The paper concludes by outlining the critical challenges and promising research directions for MoE, such as training stability and load balancing, scalability and communication overhead, expert specialization and collaboration, sparse activation and computational efficiency, generalization and robustness, interpretability and transparency, optimal expert architecture, and integration with existing frameworks.
Reference: https://arxiv.org/abs/2407.06204