3 papers
cs.CL2025
Global PIQA: Evaluating Commonsense Reasoning Across 100+ Languages and Cultures
Tyler A. Chang, Catherine Arnett, Abdelrahman Sadallah +377
To date, there exist almost no culturally-specific evaluation benchmarks for large language models (LLMs) that cover a large number of languages and cultures. In this paper, we pre…
cs.CV2024
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…
cs.LG2024
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…