47 citations · 47 across the 3 of their papers we have counts for
3 papers
SoftSRV: Learn to Generate Targeted Synthetic Data
Giulia DeSalvo, Jean-Fracois Kagy, Lazaros Karydas +2
We present a novel framework, SoftSRV, that is used to generate targeted synthetic fine-tuning data for improving task-specific model performance. Given a sample from a target dist…
SpacTor-T5: Pre-training T5 Models with Span Corruption and Replaced Token Detection
Ke Ye, Heinrich Jiang, Afshin Rostamizadeh +6
Pre-training large language models is known to be extremely resource intensive and often times inefficient, under-utilizing the information encapsulated in the training text sequen…
Batch Active Learning at Scale
Gui Citovsky, Giulia DeSalvo, Claudio Gentile +4
The ability to train complex and highly effective models often requires an abundance of training data, which can easily become a bottleneck in cost, time, and computational resourc…