Reference Model

Reference models serve as standardized frameworks for representing domains, enabling consistent modeling across various applications. Current research focuses on improving reference model fairness (e.g., mitigating racial bias in healthcare), aligning models with their implementations (e.g., in systems engineering and business intelligence), and enhancing large language model alignment through techniques like direct preference optimization and its multi-reference extensions. These advancements are crucial for ensuring reliable, unbiased, and efficient systems across diverse fields, from healthcare and manufacturing to artificial intelligence and business processes.