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GLaM: Efficient Scaling of Language Models with Mixture-of-Experts

Nan Du · Yanping Huang · Andrew Dai · Simon Tong · Dmitry Lepikhin · Yuanzhong Xu · Maxim Krikun · Yanqi Zhou · Adams Wei Yu · Orhan Firat · Barret Zoph · William Fedus · Maarten Bosma · Zongwei Zhou · Tao Wang · Emma Wang · Kellie Webster · Marie Pellat · Kevin Robinson · Kathleen Meier-Hellstern · Toju Duke · Lucas Dixon · Kun Zhang · Quoc Le · Yonghui Wu · Zhifeng Chen · Claire Cui

Hall E #208

Keywords: [ MISC: Transfer, Multitask and Meta-learning ] [ DL: Self-Supervised Learning ] [ MISC: Representation Learning ] [ APP: Language, Speech and Dialog ]


Scaling language models with more data, compute and parameters has driven significant progress in natural language processing. For example, thanks to scaling, GPT-3 was able to achieve strong results on in-context learning tasks. However, training these large dense models requires significant amounts of computing resources. In this paper, we propose and develop a family of language models named \glam (\textbf{G}eneralist \textbf{La}nguage \textbf{M}odel), which uses a sparsely activated mixture-of-experts architecture to scale the model capacity while also incurring substantially less training cost compared to dense variants. The largest \glam has 1.2 trillion parameters, which is approximately 7x larger than GPT-3. It consumes only 1/3 of the energy used to train GPT-3 and requires half of the computation flops for inference, while still achieving better overall fewshot performance across 29 NLP tasks.

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