Input Output Pair

Input-output pairs are fundamental data structures in machine learning, used to train models that map inputs to outputs. Current research focuses on improving model accuracy and efficiency, particularly through techniques like neural machine translation and optimization algorithms that leverage both instructions and exemplars to enhance prompt engineering for large language models. These advancements are crucial for various applications, including program behavior modeling, automatic prompt optimization, and solving inverse problems, ultimately improving the performance and reliability of machine learning systems across diverse scientific and engineering domains. Furthermore, research explores the theoretical limits of neural network capacity and the impact of activation functions on model performance.