4 papers · 1 filter
GROM: Gradient-Free Rapid One-Shot Machine Unlearning
Paweł Batorski, Przemysław Spurek, Paul Swoboda
Machine unlearning has become a critical capability for safely removing specific, sensitive knowledge from large language models (LLMs). Current state-of-the-art approaches primari…
PLR: Plackett-Luce for Reordering In-Context Learning Examples
Pawel Batorski, Paul Swoboda
In-context learning (ICL) adapts large language models by conditioning on a small set of ICL examples, avoiding costly parameter updates. Among other factors, performance is often…
EvoMU: Evolutionary Machine Unlearning
Pawel Batorski, Paul Swoboda
Machine unlearning aims to unlearn specified training data (e.g. sensitive or copyrighted material). A prominent approach is to fine-tune an existing model with an unlearning loss…
REBEL: Hidden Knowledge Recovery via Evolutionary-Based Evaluation Loop
Patryk Rybak, PaweÅ Batorski, Paul Swoboda +1
Machine unlearning for LLMs aims to remove sensitive or copyrighted data from trained models. However, the true efficacy of current unlearning methods remains uncertain. Standard e…