9 citations · 14 across the 2 of their papers we have counts for
2 papers
cs.CL2022★ 9 cited
Quantifying Social Biases Using Templates is Unreliable
Preethi Seshadri, Pouya Pezeshkpour, Sameer Singh
Recently, there has been an increase in efforts to understand how large language models (LLMs) propagate and amplify social biases. Several works have utilized templates for fairne…
cs.CV2019★ 5 cited
Fonts-2-Handwriting: A Seed-Augment-Train framework for universal digit classification
Vinay Uday Prabhu, Sanghyun Han, Dian Ang Yap +3
In this paper, we propose a Seed-Augment-Train/Transfer (SAT) framework that contains a synthetic seed image dataset generation procedure for languages with different numeral syste…