Knowledge Enhanced Reasoning

Knowledge-enhanced reasoning aims to improve the reasoning capabilities of machine learning models by integrating external knowledge sources, addressing limitations in current models' ability to handle complex reasoning tasks. Current research focuses on integrating knowledge through various methods, including probabilistic graphical models, logical rules extracted from large language models, and reinforcement learning techniques that encourage self-reflection and knowledge-grounded predictions. This field is significant because it enhances the reliability and interpretability of AI systems across diverse applications, from improving the safety of large language models to enabling more robust and efficient navigation in robotics.

Papers