Can expert specialization patterns in SMoES be transferred across different vision-language tasks (e.g., capti

Abstract

Abstract: A pivotal advancement in the progress of large language models (LLMs) is the emergence of the Mixture-of-Experts (MoE) LLMs. Compared to traditional LLMs, MoE LLMs can achieve higher performance with fewer parameters, but it is still hard to deploy them due to their immense parameter sizes. Different from previous weight pruning methods that rely on specifically designed hardware, this paper mainly aims to enhance the deployment efficiency of MoE LLMs by introducing plug-and-play expert-level sparsification techniques. Specifically, we propose, for the first time to our best knowledge, post-tr

Research Question

Can expert specialization patterns in SMoES be transferred across different vision-language tasks (e.g., captioning, visual grounding) while maintaining performance improvements under equal parameter count constraints?

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