Preference Model

Preference modeling aims to represent and predict human choices or rankings, crucial for aligning AI systems with human values and improving human-computer interaction. Current research focuses on improving the robustness and efficiency of these models, addressing biases like format and verbosity, and exploring various architectures including Bayesian methods, direct preference optimization, and Gaussian processes. This work is significant for advancing AI safety and trustworthiness, enhancing the effectiveness of recommendation systems, and providing a deeper understanding of human decision-making.