Zero Shot

Zero-shot learning aims to enable models to perform tasks on unseen data without any task-specific training, leveraging pre-trained knowledge to generalize to new situations. Current research focuses on improving zero-shot capabilities across diverse modalities (vision, language, audio) using large language models (LLMs), vision-language models (VLMs), and diffusion models, often incorporating techniques like chain-of-thought prompting, knowledge retrieval, and prompt engineering to enhance performance and interpretability. This field is significant because it promises more efficient and adaptable AI systems, impacting various applications from image editing and medical diagnosis to robotics and natural language processing.