Preference Label

Preference labeling in machine learning, particularly for large language models (LLMs), focuses on efficiently and accurately capturing human preferences to guide model training and evaluation. Current research emphasizes moving beyond simple binary preferences towards richer representations like continuous or distributional labels, often leveraging techniques like Direct Preference Optimization (DPO) and reinforcement learning (RL), including RL from human feedback (RLHF) and its more scalable AI-feedback counterpart (RLAIF). This work is crucial for aligning LLMs with human values and improving their performance across various tasks, addressing challenges like bias in evaluation metrics and the high cost of human annotation.