Parameter-efficient finetuning stands at the forefront of this pursuit, allowing researchers and practitioners to reuse pretrained models while minimizing their computational and resource footprints. It also al
With our limited datasets (in comparison) and lacking computing power, how do we create models that can improve on the major players at a fraction of the cost?This is where the research field of Parameter-Efficient Fine-Tuning (PEFT) comes into play. Through various techniques, which ...
With the prevalence of pre-trained language models (PLMs) and the pre-training–fine-tuning paradigm, it has been continuously shown that larger models tend to yield better performance. However, as PLMs scale up, fine-tuning and storing all the parameters is prohibitively costly and eventually be...