Knowledge Alignment

Knowledge alignment focuses on harmonizing different sources of knowledge, whether within a single model (e.g., aligning different components of a large language model's knowledge base) or across multiple models and datasets (e.g., transferring knowledge between models trained on different datasets or aligning a model's knowledge with external knowledge graphs). Current research emphasizes techniques like mutual information maximization, optimal transport, and knowledge distillation to achieve this alignment, often within the context of continual learning, federated learning, or large language model adaptation. Successful knowledge alignment improves model performance, reduces errors like hallucinations, and enables efficient knowledge transfer across diverse applications, including anomaly detection, image retrieval, and natural language processing.