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The article discusses the development of Mixture-of-Experts (MoE) Transformers, a novel neural network architecture that improves the efficiency and performance of large language models by dividing the network into specialized sub-networks or 'experts'. The article explores the benefits of MoE Transformers, such as increased capacity, faster inference, and improved task-specific performance.