LLM Policy

LLM policy research focuses on developing methods to control and improve the behavior of large language models, ensuring safe, helpful, and unbiased outputs. Current research explores techniques like iterative policy design inspired by mapmaking, reinforcement learning approaches that prioritize diverse solutions, and methods for filtering self-generated training data to enhance model performance. These advancements are crucial for mitigating risks associated with LLMs and enabling their responsible deployment across various applications, from code generation and legal advice to healthcare and autonomous systems.