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cs.CL2024
Transfer Learning for Finetuning Large Language Models
Tobias Strangmann, Lennart Purucker, Jörg K. H. Franke +3
As the landscape of large language models expands, efficiently finetuning for specific tasks becomes increasingly crucial. At the same time, the landscape of parameter-efficient fi…
cs.LG2024
One-shot World Models Using a Transformer Trained on a Synthetic Prior
Fabio Ferreira, Moreno Schlageter, Raghu Rajan +2
A World Model is a compressed spatial and temporal representation of a real world environment that allows one to train an agent or execute planning methods. However, world models a…