用智能提问精准捕捉用户偏好，让决策更准更省力。
Decisive: Guiding User Decisions with Optimal Preference Elicitation from Unstructured Documents
- 从文档提取客观评分矩阵，结合主动提问学习用户偏好
- 通过自适应配对问题，20%提升决策准确率
- 适合需要个性化推荐的复杂场景，如医疗或投资选择
决策是一项认知负荷高的任务，需从多源非结构化文档中整合信息、权衡因素并融入主观偏好。现有方法（包括大语言模型和传统决策支持系统）常因信息过载或偏好捕捉不准而失效。我们提出Decisive，一种结合文档驱动推理与贝叶斯偏好推断的交互式决策框架。该框架基于源文档提取客观选项评分矩阵，同时通过自适应选择的配对权衡问题，主动学习用户的隐含偏好向量，以最大化最终决策的信息增益。该过程高效收敛，显著降低用户负担，同时保证推荐透明且个性化。大量实验表明，该方法在多个领域上显著优于通用LLM和现有决策框架，决策准确率最高提升20%。
原文摘要 · Abstract (English)
Decision-making is a cognitively intensive task that requires synthesizing relevant information from multiple unstructured sources, weighing competing factors, and incorporating subjective user preferences. Existing methods, including large language models and traditional decision-support systems, fall short: they often overwhelm users with information or fail to capture nuanced preferences accurately. We present Decisive, an interactive decision-making framework that combines document-grounded reasoning with Bayesian preference inference. Our approach grounds decisions in an objective option-scoring matrix extracted from source documents, while actively learning a user's latent preference vector through targeted elicitation. Users answer pairwise tradeoff questions adaptively selected to maximize information gain over the final decision. This process converges efficiently, minimizing user effort while ensuring recommendations remain transparent and personalized. Through extensive experiments, we demonstrate that our approach significantly outperforms both general-purpose LLMs and existing decision-making frameworks achieving up to 20% improvement in decision accuracy over strong baselines across domains.
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