Model unlearning, right to erasure, and data removal
Newest first · 2 reviewed on this page
Search insteadMiso Kim, Georu Lee, Seungwon Jeong +1
Machine unlearning for large language models (LLMs) often assumes that a pre-defined forget set matches what the model has memorized, but this frequently breaks in realistic privacy settings where the original training data is inaccessible. We term this gap forget-set…
Zhe Liu
Large Audio-Language Models (LALMs) have recently shown strong capabilities in speech understanding and question answering (QA), but they also inherit privacy risks from large-scale training data, including the unintended memorization of sensitive information. In this work, we…
Vaidehi Patil, Peter Hase, Mohit Bansal
Evaluates methods for deleting sensitive information from trained LLMs, finding current unlearning approaches insufficient against determined adversaries.
Zheyuan Liu, Guangyao Dou, Zhaoxuan Tan +2
Surveys machine unlearning techniques for LLMs including methods for forgetting specific training data, complying with data deletion requests, and maintaining model utility.