Large Language Model Alignment

Large language model (LLM) alignment focuses on ensuring that LLMs generate outputs consistent with human values and preferences, mitigating risks associated with harmful or misleading content. Current research emphasizes methods for aligning LLMs with both general and individual preferences, exploring techniques like reinforcement learning from human feedback (RLHF), preference optimization, and instruction tuning, often employing diverse datasets and model architectures (e.g., 7B parameter models). These efforts are crucial for responsible LLM development, impacting not only the safety and trustworthiness of these powerful tools but also their broader adoption across various applications in research and industry.