Prompt Tuning

Prompt tuning is a parameter-efficient fine-tuning technique for adapting large pre-trained models, such as vision-language models (VLMs) and large language models (LLMs), to specific downstream tasks by learning small sets of parameters (prompts) rather than retraining the entire model. Current research focuses on improving prompt design for various modalities (text, image, multimodal), enhancing calibration and robustness, and exploring applications across diverse fields including image segmentation, code repair, and recommendation systems. This approach offers significant advantages in terms of computational efficiency and reduced risk of overfitting, making it a valuable tool for adapting powerful foundation models to specialized tasks with limited data.