2.3k citations · 2.3k across the 2 of their papers we have counts for
2 papers
cs.CL2022★ 1 cited
Detecting Label Errors by using Pre-Trained Language Models
Derek Chong, Jenny Hong, Christopher D. Manning
We show that large pre-trained language models are inherently highly capable of identifying label errors in natural language datasets: simply examining out-of-sample data points in…
cs.LG2021★ 2.3k cited
On the Opportunities and Risks of Foundation Models
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli +111
AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.…