What Happens When Small Is Made Smaller? Exploring the Impact of Compression on Small Data Pretrained Language Models

6 April 2024

Papers citing "What Happens When Small Is Made Smaller? Exploring the Impact of Compression on Small Data Pretrained Language Models"

5 / 5 papers shown

Title
MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition David Ifeoluwa Adelani Graham Neubig Sebastian Ruder Shruti Rijhwani Michael Beukman ... Idris Abdulmumin Odunayo Ogundepo Oreen Yousuf Tatiana Moteu Ngoli Dietrich Klakow 36 43 0 22 Oct 2022
The Low-Resource Double Bind: An Empirical Study of Pruning for Low-Resource Machine Translation Orevaoghene Ahia Julia Kreutzer Sara Hooker 107 46 0 06 Oct 2021
Towards Efficient Post-training Quantization of Pre-trained Language Models Haoli Bai Lu Hou Lifeng Shang Xin Jiang Irwin King M. Lyu MQ 71 47 0 30 Sep 2021
The Lottery Ticket Hypothesis for Pre-trained BERT Networks Tianlong Chen Jonathan Frankle Shiyu Chang Sijia Liu Yang Zhang Zhangyang Wang Michael Carbin 148 345 0 23 Jul 2020
Comparing Rewinding and Fine-tuning in Neural Network Pruning Alex Renda Jonathan Frankle Michael Carbin 222 382 0 05 Mar 2020