Prompt Token

Prompt tokens are learnable parameters added to the input of pre-trained language and vision-language models to adapt them to specific downstream tasks without extensive fine-tuning. Current research focuses on improving prompt learning efficiency and generalization by exploring various prompt architectures (e.g., shared vs. unshared tokens, spatially aligned prompts), optimization strategies (e.g., reinforcement learning, meta-learning), and initialization methods (e.g., using prototypes from downstream tasks). This approach offers a parameter-efficient way to customize large models for diverse applications, impacting fields like image classification, question answering, and medical image segmentation by improving both performance and resource utilization.