Generalization Technique

Generalization techniques in machine learning aim to develop models that perform well on unseen data, differing from the training data in distribution or characteristics. Current research focuses on understanding the underlying mechanisms of generalization in various model architectures, including large language models and neural networks, often employing techniques like fine-tuning, domain adaptation, and the incorporation of contextual information to improve robustness. This research is crucial for building reliable and adaptable AI systems across diverse real-world applications, such as improving the efficiency of training large models, enhancing the accuracy of anomaly detection, and ensuring privacy in data anonymization.