This paper introduces a novel decision-making framework that promotes consistency among decisions made by diverse models while utilizing external knowledge. Leveraging the Integer Linear Programming (ILP) framework, we map predictions from various models into globally normalized and comparable values by incorporating information about decisions' prior probability, confidence (uncertainty), and the models' expected accuracy. Our empirical study demonstrates the superiority of our approach over conventional baselines on multiple datasets.

翻译：本文提出了一种新颖的决策框架，该框架在利用外部知识的同时，促进了由不同模型做出的决策之间的一致性。借助整数线性规划（ILP）框架，我们通过整合决策的先验概率、置信度（不确定性）以及模型的预期精度信息，将来自各种模型的预测映射为全局归一化且可比较的值。我们的实证研究表明，本方法在多个数据集上优于传统基线方法。