Preference Alignment

Preference alignment in large language models (LLMs) focuses on aligning model outputs with human preferences, improving helpfulness, harmlessness, and overall quality. Current research emphasizes techniques like Direct Preference Optimization (DPO) and its variants, often incorporating token-level weighting or importance sampling to enhance efficiency and address issues like update regression. This field is crucial for responsible LLM deployment, impacting various applications from translation and text-to-speech to healthcare and robotics by ensuring models generate outputs that align with human values and expectations.