Large language models (LLMs) have shown remarkable versatility across tasks, but aligning them with individual human preferences remains challenging due to the complexity and diversity of these preferences. Existing methods often overlook the fact that preferences are multi-objective, diverse, and hard to articulate, making full alignment difficult. In response, we propose an active preference learning framework that uses binary feedback to estimate user preferences across multiple objectives. Our approach leverages Bayesian inference to update preferences efficiently and reduces user feedback through an acquisition function that optimally selects queries. Additionally, we introduce a parameter to handle feedback noise and improve robustness. We validate our approach through theoretical analysis and experiments on language generation tasks, demonstrating its feedback efficiency and effectiveness in personalizing model responses.
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