June 5, 2024, 4:43 a.m. | Shwai He, Daize Dong, Liang Ding, Ang Li

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.02500v1 Announce Type: new
Abstract: Scaling large language models has revolutionized the performance across diverse domains, yet the continual growth in model size poses significant challenges for real-world deployment. The Mixture of Experts (MoE) approach addresses this by dynamically selecting and activating only a subset of experts, significantly reducing computational costs while maintaining high performance. However, MoE introduces potential redundancy (e.g., parameters) and extra costs (e.g., communication overhead). Despite numerous compression techniques developed for mitigating the redundancy in dense models, …

abstract arxiv challenges compression computational continual costs cs.ai cs.lg deployment diverse domains experts framework growth language language models large language large language models mixture of experts moe performance scaling through type world

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