Language Model Alignment

Language model alignment focuses on aligning large language models (LLMs) with human values and preferences, aiming to make them more helpful, harmless, and truthful. Current research emphasizes efficient alignment methods, such as direct preference optimization (DPO) and its variants, which avoid the complexities and instability of traditional reinforcement learning approaches. These techniques often leverage preference data, sometimes generated through self-play or other automated methods, to iteratively refine the model's behavior. This field is crucial for ensuring the safe and beneficial deployment of LLMs in various applications, impacting both the trustworthiness of AI systems and the broader scientific understanding of human-AI interaction.