Параметрлік тиімді баптау

AbstractIn recent years, pretrained models revolutionized the paradigm of natural language understanding (NLU), where we append a randomly initialized classification head after the pretrained backbone, e.g. BERT, and finetune the …...

AbstractParameter-Efficient Tuning (PET) has shown remarkable performance by fine-tuning only a small number of parameters of the pre-trained language models (PLMs) for the downstream tasks, while it is also possible …...

AbstractUsing pre-trained language models to implement classifiers from small to modest amounts of training data is an area of active research. The ability of large language models to generalize from …...

AbstractParameter-efficient tuning aims at updating only a small subset of parameters when adapting a pretrained model to downstream tasks. In this work, we introduce PASTA, in which we only modify …...