Calibrate Before Use: Enhancing Language Model Performance with Few-Shot Learning
Calibrate before use: improving few-shot performance of language models – In the realm of language models, “calibrate before use” has emerged as a crucial concept for unlocking their full potential in few-shot learning tasks. By refining their predictions, calibration techniques empower language models to achieve greater accuracy, precision, and recall even with limited training data. […]
Continue Reading