Template Based

Template-based methods are increasingly used across various fields to improve and evaluate machine learning models, particularly in scenarios with limited data or complex tasks. Current research focuses on leveraging templates to enhance model explainability (e.g., in medical image analysis), improve the performance of language models through targeted probing, and facilitate efficient data augmentation for training. These techniques offer significant potential for advancing model development and evaluation, particularly in resource-constrained domains, by providing structured approaches to data generation, analysis, and bias detection.