{"ID":2848356,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.25799","arxiv_id":"2510.25799","title":"LISTEN to Your Preferences: An LLM Framework for Multi-Objective Selection","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 (LLM-based Iterative Selection with Trade-off Evaluation from Natural-language), an agentic LLM-based framework that treats the LLM as a decision-making agent capable of iteratively refining its internal preference model and taking actions (e.g., proposing utilities or selecting candidates) to maximize alignment with a user's implicit goals. 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 overall. This work explores a promising direction for steering complex multi-objective decisions directly with natural language, reducing the cognitive burden of traditional preference elicitation. Code is available at https://github.com/AdamJovine/LISTEN.","short_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 (LLM-based Iterative Selection with Trade-off Evaluation from Natural-language)...","url_abs":"https://arxiv.org/abs/2510.25799","url_pdf":"https://arxiv.org/pdf/2510.25799v2","authors":"[\"Adam S. Jovine\",\"Tinghan Ye\",\"Francis Bahk\",\"Jingjing Wang\",\"Matthew Ford\",\"David B. Shmoys\",\"Peter I. Frazier\"]","published":"2025-10-29T03:17:37Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[\"Large Language Model\"]","has_code":false,"code_links":[{"ID":607615,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2848356,"paper_url":"https://arxiv.org/abs/2510.25799","paper_title":"LISTEN to Your Preferences: An LLM Framework for Multi-Objective Selection","repo_url":"https://github.com/AdamJovine/LISTEN","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}