Does the layer-dependent expert specialization learned by SMoES generalize to out-of-distribution multimodal r

Abstract

Abstract: Mixture-of-Experts (MoE) Multimodal large language models (MLLMs) excel at vision-language tasks, but they suffer from high computational inefficiency. To reduce inference overhead, expert skipping methods have been proposed to deactivate redundant experts based on the current input tokens. However, we find that applying these methods-originally designed for unimodal large language models (LLMs)-to MLLMs results in considerable performance degradation. This is primarily because such methods fail to account for the heterogeneous contributions of experts across MoE layers and modality-specific b

Research Question

Does the layer-dependent expert specialization learned by SMoES generalize to out-of-distribution multimodal reasoning tasks (e.g., VCR or SNLI-VE) without retraining, measured by accuracy degradation compared to in-distribution GQA/NLVR2 scores?

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