Task Selection

Task selection focuses on strategically choosing the most beneficial tasks for training machine learning models, aiming to improve efficiency and performance, particularly in transfer learning and meta-learning scenarios. Current research explores various methods for task selection, including those based on task embeddings, token-wise similarity, and the use of instruction information, with a growing emphasis on addressing data noise and imbalanced datasets. Effective task selection is crucial for optimizing resource utilization and enhancing the robustness and generalization capabilities of machine learning models across diverse applications, including dialogue systems and few-shot learning.