Learning Transferable Latent User Preferences for Human-Aligned Decision Making

Large language models (LLMs) are increasingly used as reasoning modules in many applications, yet they often struggle to produce human-aligned decisions. Human-aligned decision-making requires accounting for both explicitly stated goals and latent user preferences that shape how ambiguous situations should be resolved. Existing approaches either learn these preferences through continual per-interaction feedback, which is costly for both the user and the agent, or keep them implicit in dialogue transcripts or example sets tied to a single model. We present CLIPR (Conversational Learning for Inferring Preferences and Reasoning), a framework that represents user's preferences as an explicit, compact set of natural-language rules used at inference time for human-aligned decision-making. CLIPR elicits these rules from conversational input, monitors decision accuracy, and consults the user when performance degrades unexpectedly. CLIPR revises the learned rules, based on the conversation, only if it does not reduce accuracy on scenarios already processed. Evaluations on three preference-ambiguous datasets show that CLIPR matches or outperforms baselines while using up to 96% fewer LLM calls than continual-feedback preference learning. Our results also show that rules learned by one LLM transfer unchanged to others, and that our approach can recover from corrupted rules. In a user study with 30 participants, CLIPR outperformed baselines and was rated as requiring less effort than continual-feedback learning.