Alignment Problem

The alignment problem in artificial intelligence focuses on ensuring that advanced models, particularly large language models (LLMs) and diffusion models, behave in ways consistent with human values and intentions. Current research emphasizes improving reward models, developing more robust evaluation metrics (moving beyond deterministic point estimates to probabilistic frameworks), and exploring various alignment techniques, including preference optimization, knowledge distillation, and contrastive learning, often applied within fine-tuning or training-free frameworks. Successfully addressing the alignment problem is crucial for the safe and ethical deployment of powerful AI systems across diverse applications, ranging from healthcare and drug discovery to robotics and social media moderation.