Shot Example

Few-shot learning (FSL) focuses on training machine learning models, particularly large language models (LLMs), to effectively perform tasks using only a small number of examples. Current research emphasizes improving the selection and utilization of these few-shot examples, exploring techniques like active learning, reinforcement learning, and the development of novel prompting strategies to optimize model performance. This area is significant because it addresses the limitations of data-hungry models, enabling efficient adaptation to new tasks and potentially reducing the need for extensive training datasets in various applications, including natural language processing, computer vision, and robotics.