Knowledge Fusion

Knowledge fusion aims to combine information from diverse sources, such as multiple models, datasets, or knowledge graphs, to improve the performance and capabilities of machine learning systems. Current research focuses on developing effective fusion strategies, including techniques like weighted averaging of model parameters, prototypical knowledge transfer, and hierarchy-oriented prompting, often within the context of specific model architectures such as large language models and graph neural networks. This field is significant because it addresses limitations of individual models by leveraging complementary strengths, leading to improved accuracy, robustness, and efficiency across various applications, including traffic prediction, personalized education, and biomedical document retrieval.