Prompt Learning Method

Prompt learning is a technique for adapting pre-trained large language models (LLMs) and vision-language models (VLMs) to specific downstream tasks by learning optimal text or visual embeddings, often called "prompts," rather than extensive fine-tuning. Current research focuses on improving generalization to unseen tasks and data, addressing overfitting, and enhancing model explainability through techniques like cascading prompts, external layers, and knowledge graph integration. This approach offers significant advantages in resource-constrained environments and few-shot learning scenarios, impacting fields ranging from image classification and fake news detection to travel choice modeling and medical diagnosis.