Abstracting human preferences into computational objectives is essential for aligning AI systems, yet fundamentally challenging due to the complexity and context-dependence of human values. This talk examines how preferences are captured through human annotation and translated into reward models for reinforcement learning from human feedback. While enabling state-of-the-art chatbots, I'll ...
