3 papers
cs.LG2026
REBEL: Hidden Knowledge Recovery via Evolutionary-Based Evaluation Loop
Patryk Rybak, Paweł 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…
cs.CV2026
ReLAPSe: Reinforcement-Learning-trained Adversarial Prompt Search for Erased concepts in unlearned diffusion models
Ignacy Kolton, Kacper Marzol, Paweł Batorski +3
Machine unlearning is a key defense mechanism for removing unauthorized concepts from text-to-image diffusion models, yet recent evidence shows that latent visual information often…
cs.CL2025
GPS: General Per-Sample Prompter
Pawel Batorski, Paul Swoboda
LLMs are sensitive to prompting, with task performance often hinging on subtle, sometimes imperceptible variations in phrasing. As a result, crafting effective prompts manually rem…