Scaling Vision-Language Models with Sparse Mixture of Experts (2023.findings-emnlp)
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| Challenge: | a study explores the effectiveness of mixture-of-experts (MoE) techniques in scaling vision-language models . alayrac and colleagues demonstrate the effectiveness and performance of MoE in scaling VLMs . |
| Approach: | They propose to use sparsely-gated mixture-of-experts techniques to scale vision-language models . they show that MoE can achieve state-of the-art performance over dense models a range of benchmarks . |
| Outcome: | The proposed approach achieves state-of-the-art performance over dense models of equivalent computational cost. |
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| Challenge: | Sparsely gated Mixture of Experts (MoE) models are a compute-efficient method to scale model capacity for multilingual machine translation tasks. |
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| Challenge: | Recent advances in multimodal large language models have remained opaque. |
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| Challenge: | Mixture-of-Experts (MoE) has gained increasing popularity as a framework for scaling up large language models. |
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