What is the impact of token-level guided routing on inference latency and cross-modal reasoning accuracy in Mo

Abstract

Abstract: Abstract In the past years, multimodal large language models (MLLMs) have demonstrated remarkable performance in tasks such as visual question answering and visual understanding and reasoning. However, the extensive model size and high training and inference costs have hindered the widespread application of MLLMs in academia and industry. Thus, studying efficient and lightweight MLLMs has enormous potential, especially in edge computing scenarios. In this survey, we provide a comprehensive and systematic review of the current state of efficient MLLMs. Specifically, this survey summarizes the t

Research Question

What is the impact of token-level guided routing on inference latency and cross-modal reasoning accuracy in MoE vision-language models compared to dense baselines on the MMBench and SEED-Bench benchmarks?

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