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cs.LG2026
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…
cs.LG2026
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…
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…