Knowledge Infusion

Knowledge infusion aims to enhance the capabilities of machine learning models, particularly large language models (LLMs), by integrating external knowledge sources like knowledge graphs and ontologies. Current research focuses on developing efficient methods for incorporating this knowledge, often employing techniques like contrastive learning, graph neural networks, and prompt engineering to improve model performance on tasks such as question answering, stance detection, and synthetic data generation. This work is significant because it addresses limitations of LLMs in handling domain-specific knowledge and low-resource scenarios, leading to more accurate, explainable, and privacy-preserving AI systems across various applications.