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cs.LG2024
Concept-ROT: Poisoning Concepts in Large Language Models with Model Editing
Keltin Grimes, Marco Christiani, David Shriver +1
Model editing methods modify specific behaviors of Large Language Models by altering a small, targeted set of network weights and require very little data and compute. These method…
cs.LG2024
Gone but Not Forgotten: Improved Benchmarks for Machine Unlearning
Keltin Grimes, Collin Abidi, Cole Frank +1
Machine learning models are vulnerable to adversarial attacks, including attacks that leak information about the model's training data. There has recently been an increase in inter…
cs.LG2024
The SaTML '24 CNN Interpretability Competition: New Innovations for Concept-Level Interpretability
Stephen Casper, Jieun Yun, Joonhyuk Baek +13
Interpretability techniques are valuable for helping humans understand and oversee AI systems. The SaTML 2024 CNN Interpretability Competition solicited novel methods for studying…