Preference Dataset

Preference datasets are collections of human judgments comparing different model outputs, used to align large language models (LLMs) with human preferences. Current research focuses on improving the efficiency and quality of these datasets, exploring methods like auctions to optimize annotation costs and developing metrics to compare datasets' effectiveness. This work is crucial for advancing reinforcement learning from human feedback (RLHF) and other preference-based learning algorithms (e.g., DPO, PPO), ultimately leading to more helpful and aligned AI systems. The development of larger, higher-quality, and more diverse preference datasets is a key area of ongoing effort.