Better Alignment

Better alignment in large language models (LLMs) focuses on ensuring model outputs consistently reflect human values and intentions, addressing issues like harmful content generation and biases. Current research emphasizes developing more efficient and robust alignment techniques, exploring methods like Direct Preference Optimization (DPO), Reinforcement Learning from Human Feedback (RLHF), and iterative self-improvement paradigms, often incorporating novel training strategies and data generation methods to improve model safety and performance. These advancements are crucial for building trustworthy and beneficial AI systems, impacting both the development of safer LLMs and the broader field of AI safety research.