17 citations · 20 across the 6 of their papers we have counts for
4 papers · 1 filter
Adaptation Odyssey in LLMs: Why Does Additional Pretraining Sometimes Fail to Improve?
Fırat Öncel, Matthias Bethge, Beyza Ermis +3
In the last decade, the generalization and adaptation abilities of deep learning models were typically evaluated on fixed training and test distributions. Contrary to traditional d…
Identifying latent state transition in non-linear dynamical systems
Çağlar Hızlı, Çağatay Yıldız, Matthias Bethge +2
This work aims to improve generalization and interpretability of dynamical systems by recovering the underlying lower-dimensional latent states and their time evolutions. Previous…
Reflecting on the State of Rehearsal-free Continual Learning with Pretrained Models
Lukas Thede, Karsten Roth, Olivier J. Hénaff +2
With the advent and recent ubiquity of foundation models, continual learning (CL) has recently shifted from continual training from scratch to the continual adaptation of pretraine…
Investigating Continual Pretraining in Large Language Models: Insights and Implications
Çağatay Yıldız, Nishaanth Kanna Ravichandran, Nitin Sharma +2
Continual learning (CL) in large language models (LLMs) is an evolving domain that focuses on developing efficient and sustainable training strategies to adapt models to emerging k…