GLaM (Efficient Scaling of Language Models with Mixture-of-Experts) is a novel approach to scaling up language models by leveraging a technique called Mixture-of-Experts. This method involves combining multiple smaller models, or "experts," each specializing in different aspects of language understanding, into a single large model. By doing so, GLaM achieves significant improvements in efficiency and scalability while maintaining high performance on various natural language processing tasks.
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