Context Sample

Context sample methods leverage a set of example input-output pairs to guide model predictions, improving performance on tasks with limited data or imbalanced distributions. Current research focuses on optimizing the selection and utilization of these samples, particularly within large language models (LLMs) and transformer architectures, exploring techniques like efficient retrieval strategies and feature adaptation to enhance model robustness and accuracy. This approach holds significant promise for improving the efficiency and generalizability of machine learning models across diverse applications, including medical image analysis and text generation.