Behavioral Alignment

Behavioral alignment in artificial intelligence focuses on aligning the behavior of AI systems, particularly large language models (LLMs) and multimodal models, with human behavior and preferences. Current research emphasizes developing metrics to quantify this alignment, often comparing AI system outputs and error patterns to those of humans, and exploring methods like direct preference optimization and reinforcement learning from human feedback (RLHF) to improve alignment. These efforts are crucial for building trustworthy and reliable AI systems, improving the accuracy and user satisfaction of AI-driven applications like conversational recommendation systems, and advancing our understanding of complex human behaviors through the lens of AI modeling.