---
title: "LISTEN to Your Preferences: An LLM Framework for Multi-Objective Selection"
authors:
  - "Adam S. Jovine"
  - "Tinghan Ye"
  - "Francis Bahk"
  - "Jingjing Wang"
  - "Matthew Ford"
  - "David B. Shmoys"
  - "Peter I. Frazier"
doi: "10.24963/ijcai.2026/403"
url: "https://ijcai.org/proceedings/2026/403"
pdf: "https://ijcai.org/proceedings/2026/0403.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "3621-3629"
track: "Main Track"
keywords:
  - "Natural Language Processing: Language models"
  - "Humans and AI: Personalization and user modeling"
  - "Knowledge Representation and Reasoning: Preference modelling and preference-based reasoning"
---

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

Adam S. Jovine, Tinghan Ye, Francis Bahk, Jingjing Wang, Matthew Ford, David B. Shmoys, Peter I. Frazier

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 3621-3629. DOI: [10.24963/ijcai.2026/403](https://doi.org/10.24963/ijcai.2026/403)

## 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.