54 citations · 150 across the 28 of their papers we have counts for
4 papers · 1 filter
How Reliable are Causal Probing Interventions?
Marc Canby, Adam Davies, Chirag Rastogi +1
Causal probing aims to analyze foundation models by examining how intervening on their representation of various latent properties impacts their outputs. Recent works have cast dou…
Analyzing the Performance of Large Language Models on Code Summarization
Rajarshi Haldar, Julia Hockenmaier
Large language models (LLMs) such as Llama 2 perform very well on tasks that involve both natural language and source code, particularly code summarization and code generation. We…
ViT-MUL: A Baseline Study on Recent Machine Unlearning Methods Applied to Vision Transformers
Ikhyun Cho, Changyeon Park, Julia Hockenmaier
Machine unlearning (MUL) is an arising field in machine learning that seeks to erase the learned information of specific training data points from a trained model. Despite the rece…
Attack and Reset for Unlearning: Exploiting Adversarial Noise toward Machine Unlearning through Parameter Re-initialization
Yoonhwa Jung, Ikhyun Cho, Shun-Hsiang Hsu +1
With growing concerns surrounding privacy and regulatory compliance, the concept of machine unlearning has gained prominence, aiming to selectively forget or erase specific learned…