Train Test

Train-test discrepancies, where differences between training and testing data negatively impact model performance, are a significant challenge across various machine learning domains. Current research focuses on mitigating these discrepancies through techniques like developing scale-invariant models, refining data preprocessing to reduce class overlap, and improving the generalization capabilities of models, particularly in long-sequence processing (e.g., LLMs) and weakly supervised learning. Addressing these issues is crucial for improving the reliability and robustness of machine learning models in real-world applications, ranging from information retrieval and image recognition to natural language processing.