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cs.CL2022
Knowledge Distillation Transfer Sets and their Impact on Downstream NLU Tasks
Charith Peris, Lizhen Tan, Thomas Gueudre +3
Teacher-student knowledge distillation is a popular technique for compressing today's prevailing large language models into manageable sizes that fit low-latency downstream applica…
cs.CL2022★ 64 cited
Alexa Teacher Model: Pretraining and Distilling Multi-Billion-Parameter Encoders for Natural Language Understanding Systems
Jack FitzGerald, Shankar Ananthakrishnan, Konstantine Arkoudas +38
We present results from a large-scale experiment on pretraining encoders with non-embedding parameter counts ranging from 700M to 9.3B, their subsequent distillation into smaller m…