Label Mapping

Label mapping focuses on establishing effective correspondences between input data and output labels, a crucial step in various machine learning tasks. Current research emphasizes improving the robustness and efficiency of this mapping, particularly in scenarios with noisy or heterogeneous data, using techniques like input mapping calibration, diffusion models for handling noisy labels, and novel demonstration selection strategies for in-context learning. These advancements are significant for improving the accuracy and generalizability of models across diverse applications, from image synthesis and natural language processing to engineering design optimization.