English(EN) LISTEN to Your Preferences: An LLM Framework for Multi-Objective Selection

大语言模型框架 LISTEN 助力多目标决策

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-02 04:00

研究人员开发了 LISTEN，一个使用大语言模型（LLMs）帮助用户做出具有多个竞争性目标的复杂决策的新框架。该框架采用迭代算法来完善大语言模型对隐含偏好的理解，减轻了传统决策过程的认知负担。在航班预订和购物等任务上进行评估，LISTEN 证明了其在使大语言模型决策与用户目标保持一致方面的有效性。 AI

影响通过学习用户隐含偏好，为大语言模型在复杂决策任务中提供了一种新颖的辅助方法。

排序理由该集群包含一篇详细介绍新框架和算法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇详细介绍新框架和算法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, product; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: 85 days old; Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.CL TIER_1 English(EN) · Adam S. Jovine, Tinghan Ye, Francis Bahk, Jingjing Wang, Matthew Ford, David B. Shmoys, Peter I. Frazier · 2026-06-02 04:00

倾听您的偏好：一个用于多目标选择的LLM框架

arXiv:2510.25799v3 Announce Type: replace 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 intr…

本文如何被排名

报道来源 [1]

倾听您的偏好：一个用于多目标选择的LLM框架

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