Semantic Shift

Semantic shift, the change in word or concept meaning over time or across contexts, is a significant challenge in natural language processing and machine learning. Current research focuses on detecting and mitigating these shifts, particularly in anomaly detection, continual learning, and cross-domain generalization, employing techniques like contrastive learning, knowledge distillation, and adaptive prompting to improve model robustness. Addressing semantic shift is crucial for building reliable and generalizable AI systems, impacting applications ranging from financial analysis and medical diagnosis to social science research and the development of more human-like language models.