Sensitivity Score

Sensitivity scores quantify the influence of individual data points or model parameters on a system's output, aiming to improve model robustness, accuracy, and efficiency. Current research focuses on developing efficient algorithms for computing these scores across diverse applications, including bias detection in large language models, improving the training of physics-informed neural networks, and enhancing the accuracy of few-shot learners. These advancements are significant because they enable better understanding and mitigation of biases, improved model performance in various domains, and more efficient computation in complex systems.