用自然语言让大模型帮你多目标选最优解，省去繁琐偏好描述。
LISTEN to Your Preferences: An LLM Framework for Multi-Objective Selection
- 大模型作为决策代理，通过迭代优化内部偏好模型来选最佳方案。
- 针对不同场景，分别采用参数化与锦标赛式选择，性能更稳定。
- 适合需要多目标权衡的复杂决策任务，如订票、购物、排考等。
人类专家在大量选项中进行多目标选择时常因难以形式化复杂隐性偏好而受阻。为此，我们提出 LISTEN（基于大模型的自然语言迭代选择与权衡评估），一种将大模型视为决策代理的框架，可迭代优化其内部偏好模型并执行提议效用或选择候选等动作，以最大化与用户隐性目标的对齐。为适应大模型上下文窗口和推理成本限制，我们设计了两种迭代算法：LISTEN-U 利用大模型优化参数化效用函数；LISTEN-T 采用非参数化的锦标赛式选择，对小批量解进行筛选。在航班预订、购物和考试安排等多样任务上评估显示，当偏好具备参数对齐性时（我们引入新颖的一致性度量衡量），LISTEN-U 表现更优；而 LISTEN-T 整体更具鲁棒性。该研究探索了直接通过自然语言引导复杂多目标决策的新方向，显著降低传统偏好获取的认知负担。代码已开源：https://github.com/AdamJovine/LISTEN；数据集见：https://huggingface.co/datasets/AdamJovine/LISTEN-benchmark。
原文摘要 · Abstract (English)
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; data is available at https://huggingface.co/datasets/AdamJovine/LISTEN-benchmark.
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