<ul><li>Mixture of Experts (MoE) has emerged as a pivotal architectural paradigm for efficient scaling of Large Language Models (LLMs), operating through selective activation of parameter subsets for each input token.</li><li>In this paper, the authors introduce Mixture of Latent Experts (MoLE), a novel parameterization methodology that facilitates the mapping of specific experts into a shared latent space.</li><li>The MoLE architecture significantly reduces parameter count and computational requirements, addressing challenges such as excessive memory utilization and communication overhead during training and inference.</li><li>Empirical evaluations demonstrate that MoLE achieves performance comparable to standard MoE implementations while substantially reducing resource requirements.</li></ul>

Beyond Standard MoE: Mixture of Latent Experts for Resource-Efficient Language Models

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