5.7k citations · 8.2k across the 29 of their papers we have counts for
4 papers · 1 filter
GLaM: Efficient Scaling of Language Models with Mixture-of-Experts
Nan Du, Yanping Huang, Andrew M. Dai +24
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 ach…
Beyond Distillation: Task-level Mixture-of-Experts for Efficient Inference
Sneha Kudugunta, Yanping Huang, Ankur Bapna +4
Sparse Mixture-of-Experts (MoE) has been a successful approach for scaling multilingual translation models to billions of parameters without a proportional increase in training com…
Scaling Laws for Neural Machine Translation
Behrooz Ghorbani, Orhan Firat, Markus Freitag +5
We present an empirical study of scaling properties of encoder-decoder Transformer models used in neural machine translation (NMT). We show that cross-entropy loss as a function of…
GSPMD: General and Scalable Parallelization for ML Computation Graphs
Yuanzhong Xu, HyoukJoong Lee, Dehao Chen +13
We present GSPMD, an automatic, compiler-based parallelization system for common machine learning computations. It allows users to write programs in the same way as for a single de…