Semantic Prompt

Semantic prompting leverages textual or other semantic information to guide and enhance the performance of machine learning models, particularly in scenarios with limited data or complex tasks. Current research focuses on developing methods to effectively integrate semantic prompts into various model architectures, including large language models and vision transformers, for applications such as few-shot learning, continual learning, and knowledge distillation. This approach improves model generalization, reduces the need for extensive training data, and addresses challenges like catastrophic forgetting and spurious correlations, leading to more robust and efficient AI systems across diverse domains. The impact extends to improving the accuracy and efficiency of various applications, from image segmentation and object recognition to time series forecasting and video quality assessment.