215 citations · 219 across the 3 of their papers we have counts for
6 papers · 1 filter
Staged Training for Transformer Language Models
Sheng Shen, Pete Walsh, Kurt Keutzer +3
The current standard approach to scaling transformer language models trains each model size from a different random initialization. As an alternative, we consider a staged training…
Expected Validation Performance and Estimation of a Random Variable's Maximum
Jesse Dodge, Suchin Gururangan, Dallas Card +2
Research in NLP is often supported by experimental results, and improved reporting of such results can lead to better understanding and more reproducible science. In this paper we…
Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus
Jesse Dodge, Maarten Sap, Ana Marasović +5
Large language models have led to remarkable progress on many NLP tasks, and researchers are turning to ever-larger text corpora to train them. Some of the largest corpora availabl…
The Right Tool for the Job: Matching Model and Instance Complexities
Roy Schwartz, Gabriel Stanovsky, Swabha Swayamdipta +2
As NLP models become larger, executing a trained model requires significant computational resources incurring monetary and environmental costs. To better respect a given inference…
Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping
Jesse Dodge, Gabriel Ilharco, Roy Schwartz +3
Fine-tuning pretrained contextual word embedding models to supervised downstream tasks has become commonplace in natural language processing. This process, however, is often brittl…
RNN Architecture Learning with Sparse Regularization
Jesse Dodge, Roy Schwartz, Hao Peng +1
Neural models for NLP typically use large numbers of parameters to reach state-of-the-art performance, which can lead to excessive memory usage and increased runtime. We present a…