Learning Transferable Latent User Preferences for Human-Aligned Decision Making
CLIPR is a framework that learns explicit, natural language rules representing hidden user preferences from conversations. It elicits these rules from dialogue and uses them at inference time to guide the LLM toward human-aligned decisions. This method outperforms costly continual feedback by learning transferable rules that generalize across different scenarios and models.