Learning Transferable Latent User Preferences for Human-Aligned Decision Making

This paper introduces CLIPR, a framework designed to enable Large Language Models (LLMs) to make human-aligned decisions by inferring latent user preferences from limited interactions. It addresses the challenge of LLMs struggling with human alignment and the limitations of existing approaches in generalizing preferences across tasks.