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cs.CL2023
SlimFit: Memory-Efficient Fine-Tuning of Transformer-based Models Using Training Dynamics
Arash Ardakani, Altan Haan, Shangyin Tan +4
Transformer-based models, such as BERT and ViT, have achieved state-of-the-art results across different natural language processing (NLP) and computer vision (CV) tasks. However, t…
cs.CL2023
Model-Generated Pretraining Signals Improves Zero-Shot Generalization of Text-to-Text Transformers
Linyuan Gong, Chenyan Xiong, Xiaodong Liu +5
This paper explores the effectiveness of model-generated signals in improving zero-shot generalization of text-to-text Transformers such as T5. We study various designs to pretrain…