GUIDE：生成式效用推断与决策引擎

中文摘要 AI 辅助

衡量人类用户的偏好仍然是AI对齐中的一个基本挑战。现有的偏好 elicitation（引出）方法难以高效地发现多维偏好，或难以将这些推断准确锚定在领域知识中。为解决这一问题，我们引入了GUIDE，一种由LLM驱动的引出架构，通过对话推断用户偏好，结合贝叶斯自适应采样进行问题选择，以及符号表示学习来初始化领域特定的偏好模型。GUIDE通过一个可扩展的变换类型系统，将自适应采样泛化到各种引出问题，该系统作用于参数化的偏好状态。GUIDE通过使用符号规则学习来捕获世界知识，并基于决策备选方案的数据设置偏好维度的先验，从而通过初始化过程生成领域特定的偏好表示。该架构提供了可观测性和可操控性，以促进部署和分析引出过程。在投资组合优化的计算机模拟实验中，与先前工作、仅使用LLM的基线以及消融的GUIDE版本相比，GUIDE在早期引出交互中跨用户角色一致地改善了冷启动性能，并最小化了推荐遗憾。

英文摘要

Measuring the preferences of human users remains a fundamental challenge of AI alignment. Existing elicitation approaches struggle to efficiently discover multidimensional preferences or accurately ground these inferences in domain knowledge. To address this, we introduce GUIDE, an LLM-driven elicitation architecture that infers user preferences through conversations by combining Bayesian adaptive sampling for question selection and symbolic representation learning to initialize domain-specific preference models. GUIDE generalizes adaptive sampling to diverse elicitation questions through an extensible type system of transforms on a parameterized preference state. GUIDE produces domain-specific preference representations through an initialization process using symbolic rule-based learning to capture world knowledge and set priors over preference dimensions grounded in data about decision alternatives. The architecture provides observability and steerability to facilitate deployment and analyze elicitation processes. In silico experiments on investment portfolio optimization demonstrate that GUIDE improves cold-start and minimizes recommendation regret consistently within early elicitation interactions across user personas compared to prior work, LLM-only baselines, and ablated GUIDE versions.

发表机构

• University of Chicago（芝加哥大学）
• Centaur AI Institute（Centaur AI研究所）
• Carnegie Mellon University（卡内基梅隆大学）
• Booth School of Business（布斯商学院）

机构由 AI 辅助整理，请以论文原文为准。