LISTEN to Your Preferences: An LLM Framework for Multi-Objective Selection

• URL: http://arxiv.org/abs/2510.25799v1
• Date: Wed, 29 Oct 2025 03:17:37 GMT
• Title: LISTEN to Your Preferences: An LLM Framework for Multi-Objective Selection
• Authors: Adam S. Jovine, Tinghan Ye, Francis Bahk, Jingjing Wang, David B. Shmoys, Peter I. Frazier,
• Abstract summary: We introduce LISTEN, a framework that leverages a Large Language Model (LLM) as a zero-shot preference oracle.<n>We show LISTEN-U excels when preferences are parametrically aligned, while LISTEN-T offers more robust performance.<n>This work explores a promising direction for steering complex multi-objective decisions directly with natural language.
• Score: 8.355352653283516
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human experts often struggle to select the best option from a large set of items with multiple competing objectives, a process bottlenecked by the difficulty of formalizing complex, implicit preferences. To address this, we introduce LISTEN, a framework that leverages a Large Language Model (LLM) as a zero-shot preference oracle, guided only by an expert's high-level priorities in natural language. To operate within LLM constraints like context windows and inference costs, we propose two iterative algorithms: LISTEN-U, which uses the LLM to refine a parametric utility function, and LISTEN-T, a non-parametric method that performs tournament-style selections over small batches of solutions. Evaluated on diverse tasks including flight booking, shopping, and exam scheduling, our results show LISTEN-U excels when preferences are parametrically aligned (a property we measure with a novel concordance metric), while LISTEN-T offers more robust performance. This work explores a promising direction for steering complex multi-objective decisions directly with natural language, reducing the cognitive burden of traditional preference elicitation.

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