1.2k citations · 3.2k across the 19 of their papers we have counts for
6 papers · 1 filter
Scaling Instruction-Finetuned Language Models
Hyung Won Chung, Le Hou, Shayne Longpre +32
Finetuning language models on a collection of datasets phrased as instructions has been shown to improve model performance and generalization to unseen tasks. In this paper we expl…
Building Machine Translation Systems for the Next Thousand Languages
Ankur Bapna, Isaac Caswell, Julia Kreutzer +21
In this paper we share findings from our effort to build practical machine translation (MT) systems capable of translating across over one thousand languages. We describe results i…
LaMDA: Language Models for Dialog Applications
Romal Thoppilan, Daniel De Freitas, Jamie Hall +57
We present LaMDA: Language Models for Dialog Applications. LaMDA is a family of Transformer-based neural language models specialized for dialog, which have up to 137B parameters an…
Mixture-of-Experts with Expert Choice Routing
Yanqi Zhou, Tao Lei, Hanxiao Liu +7
Sparsely-activated Mixture-of-experts (MoE) models allow the number of parameters to greatly increase while keeping the amount of computation for a given token or a given sample un…
ST-MoE: Designing Stable and Transferable Sparse Expert Models
Barret Zoph, Irwan Bello, Sameer Kumar +5
Scale has opened new frontiers in natural language processing -- but at a high cost. In response, Mixture-of-Experts (MoE) and Switch Transformers have been proposed as an energy e…
Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning
Lianmin Zheng, Zhuohan Li, Hao Zhang +9
Alpa automates model-parallel training of large deep learning (DL) models by generating execution plans that unify data, operator, and pipeline parallelism. Existing model-parallel…