Prompt Embeddings

Prompt embeddings are learned representations used to guide large language models (LLMs) and other foundation models, such as the Segment Anything Model (SAM), towards desired outputs without extensive fine-tuning. Current research focuses on developing methods for automatically generating these embeddings, often leveraging techniques like prompt tuning, meta-learning, and contrastive learning, to improve model performance on various tasks including image segmentation, text-to-image generation, and text classification. This approach offers parameter efficiency and improved control over model behavior, impacting fields like medical image analysis and natural language processing by enabling more adaptable and controllable AI systems.