6 papers · 1 filter
Replay on Demand: An Emergent Curriculum for Balancing Adaptation and Forgetting in Continued Pretraining
Lukas Thede, Shengzhuang Chen, Stefan Winzeck +3
Continued pretraining enables language models to adapt to new domains and knowledge, but often at the cost of forgetting previously acquired capabilities. Replay can mitigate this…
MedKIT: Evaluating Knowledge Integration and Generalization in Large Language Models
Lukas Thede, Yash Kumar Atri, David Chen +4
Constantly evolving real-world knowledge necessitates models to be updated continuously. Especially in medicine, as clinical evidence changes over time, outdated knowledge can pose…
Thomson: Continual Learning of Frontier Models for SovereignAI
Shengzhuang Chen, Jerrod Parker, Yejin Bang +23
The development of frontier models is commonly perceived to be the exclusive remit of a small number of heavily funded players, creating an information, economic and power asymmetr…
Structural Pruning of Large Vision Language Models: A Comprehensive Study on Pruning Dynamics, Recovery, and Data Efficiency
Yiran Huang, Lukas Thede, Massimiliano Mancini +2
While Large Vision Language Models (LVLMs) demonstrate impressive capabilities, their substantial computational and memory requirements pose deployment challenges on resource-const…
CapTrack: Multifaceted Evaluation of Forgetting in LLM Post-Training
Lukas Thede, Stefan Winzeck, Zeynep Akata +1
Large language model (LLM) post-training enhances latent skills, unlocks value alignment, improves performance, and enables domain adaptation. Unfortunately, post-training is known…
Attentive multilayer fusion for vision transformers
Laure Ciernik, Marco Morik, Lukas Thede +4
With the rise of large-scale foundation models, efficiently adapting them to downstream tasks remains a central challenge. Linear probing, which freezes the backbone and trains a l…