3 papers
cs.LG2026
Learning What to Forget: Improving LLM Unlearning via Learned Token-Level Importance
Gizem Yüce, Giorgos Nikolaou, Nicolas Flammarion
Machine unlearning aims to remove targeted knowledge from a trained model while preserving its general capabilities. For autoregressive language models, not all tokens in a forget…
cs.LG2025
Learning In-context n-grams with Transformers: Sub-n-grams Are Near-stationary Points
Aditya Varre, Gizem Yüce, Nicolas Flammarion
Motivated by empirical observations of prolonged plateaus and stage-wise progression during training, we investigate the loss landscape of transformer models trained on in-context…
stat.ML2025
Learning Parametric Distributions from Samples and Preferences
Marc Jourdan, Gizem Yüce, Nicolas Flammarion
Recent advances in language modeling have underscored the role of preference feedback in enhancing model performance. This paper investigates the conditions under which preference…