2 papers
cs.CL2025
PARA: Parameter-Efficient Fine-tuning with Prompt Aware Representation Adjustment
Zequan Liu, Yi Zhao, Ming Tan +2
In the realm of parameter-efficient fine-tuning (PEFT) methods, while options like LoRA are available, there is a persistent demand in the industry for a PEFT approach that excels…
cs.CL2023
Improving Prompt Tuning with Learned Prompting Layers
Wei Zhu, Ming Tan
Prompt tuning prepends a soft prompt to the input embeddings or hidden states and only optimizes the prompt to adapt pretrained models (PTMs) to downstream tasks. The previous work…