44 citations · 117 across the 26 of their papers we have counts for
6 papers · 1 filter
Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy and Research
A. Feder Cooper, Christopher A. Choquette-Choo, Miranda Bogen +34
"Machine unlearning" is a popular proposed solution for mitigating the existence of content in an AI model that is problematic for legal or moral reasons, including privacy, copyri…
Language model developers should report train-test overlap
Andy K Zhang, Kevin Klyman, Yifan Mai +4
Language models are extensively evaluated, but correctly interpreting evaluation results requires knowledge of train-test overlap which refers to the extent to which the language m…
The 2024 Foundation Model Transparency Index
Rishi Bommasani, Kevin Klyman, Sayash Kapoor +4
Foundation models are increasingly consequential yet extremely opaque. To characterize the status quo, the Foundation Model Transparency Index (FMTI) was launched in October 2023 t…
The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources
Shayne Longpre, Stella Biderman, Alon Albalak +20
Foundation model development attracts a rapidly expanding body of contributors, scientists, and applications. To help shape responsible development practices, we introduce the Foun…
Foundation Model Transparency Reports
Rishi Bommasani, Kevin Klyman, Shayne Longpre +5
Foundation models are critical digital technologies with sweeping societal impact that necessitates transparency. To codify how foundation model developers should provide transpare…
The Foundation Model Transparency Index
Rishi Bommasani, Kevin Klyman, Shayne Longpre +5
Foundation models have rapidly permeated society, catalyzing a wave of generative AI applications spanning enterprise and consumer-facing contexts. While the societal impact of fou…