Knowledge-infused learning (KIL) is a class of AI techniques that enhance machine learning models by integrating structured external knowledge, such as facts, rules, and relationships from various sources. The method aims to improve a model's ability to understand complex concepts, generalize from limited data, and provide interpretable decisions. By combining the pattern recognition capabilities of traditional data-driven models and large language models (LLMs) with the reasoning power of knowledge-based systems, KiL seeks to create more robust, capable, and understandable AI systems, with applications including natural language processing, robotics, and healthcare applications.
The theme of of the 2024 KIL workshop is on developing and using metrics, methods, and datasets for consistent, reliable, explainable and safe LLMs.