Streamlined Preference Alignment with Orpo: Multinomial Logistic Models in Deep Learning

The chapter introduces Orpo, a method that streamlines preference alignment by combining supervised fine-tuning and preference alignment into a single step using odds ratio optimization. The speakers, with a background in statistics, discuss how they applied concepts from multinomial logistic models to deep learning and language models, resulting in efficient learning of preferences. They highlight the advantages of their approach, such as requiring minimal data for alignment, preventing bias in machine learning models, and enhancing adaptability and general abilities of language models.