Preference Modeling

Preference modeling aims to align artificial intelligence systems, particularly large language models, with human values by learning and incorporating human preferences. Current research focuses on developing more expressive and efficient preference models, often employing transformer-based architectures and techniques like direct preference optimization or reinforcement learning from human feedback, to overcome limitations of traditional methods. These advancements are crucial for improving the safety, helpfulness, and overall alignment of AI systems with human intentions, impacting both the development of more robust AI and the ethical considerations surrounding their deployment.