Aligner Model

Aligner models aim to bridge the gap between machine and human representations, improving the safety, reliability, and human-likeness of AI systems. Current research focuses on techniques like parameter-efficient fine-tuning (PEFT) to align large language models (LLMs) with human preferences and values, often employing methods such as reinforcement learning from human feedback (RLHF) and direct preference optimization (DPO). These advancements are significant because they enhance the interpretability and robustness of AI, leading to more helpful and less harmful applications across diverse fields, including natural language processing and medical image analysis.