Tianqi Jiang

Less Is More: Rethinking Parameter-Efficient Fine-Tuning from a Subtractive Perspective

Currently, pretrained models are rapidly scaling in size, which substantially increases the cost of fine-tuning them for downstream tasks. To address this challenge, parameter-efficient fine-tuning (PEFT) methods have been developed to optimize a minimal set of parameters for adaptation. While curre