7 citations · 16 across the 5 of their papers we have counts for
Showing 2023Show all
2 papers · 1 filter
cs.LG2023★ 1 cited
Hazards from Increasingly Accessible Fine-Tuning of Downloadable Foundation Models
Alan Chan, Ben Bucknall, Herbie Bradley +1
Public release of the weights of pretrained foundation models, otherwise known as downloadable access \citep{solaiman_gradient_2023}, enables fine-tuning without the prohibitive ex…
cs.CY2023★ 5 cited
Open-Sourcing Highly Capable Foundation Models: An evaluation of risks, benefits, and alternative methods for pursuing open-source objectives
Elizabeth Seger, Noemi Dreksler, Richard Moulange +19
Recent decisions by leading AI labs to either open-source their models or to restrict access to their models has sparked debate about whether, and how, increasingly capable AI mode…