2.3k citations · 2.9k across the 20 of their papers we have counts for
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cs.LG2021★ 1 cited
Fast Model Editing at Scale
Eric Mitchell, Charles Lin, Antoine Bosselut +2
While large pre-trained models have enabled impressive results on a variety of downstream tasks, the largest existing models still make errors, and even accurate predictions may be…
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.…