Large Language Models (LLMs) have demonstrated impressive inferential
capabilities, with numerous research endeavors devoted to enhancing this
capacity through prompting. Despite these efforts, a unified epistemological
foundation is still conspicuously absent. Drawing inspiration from Kant's a
priori philosophy, we propose the UPAR prompting framework, designed to emulate
the structure of human cognition within LLMs. The UPAR framework is delineated
into four phases: "Understand", "Plan", "Act", and "Reflect", enabling the
extraction of structured information from complex contexts, prior planning of
solutions, execution according to plan, and self-reflection. This structure
significantly augments the explainability and accuracy of LLM inference,
producing a human-understandable and inspectable inferential trajectory.
Furthermore, our work offers an epistemological foundation for existing
prompting techniques, allowing for a possible systematic integration of these
methods. With GPT-4, our approach elevates the accuracy from COT baseline of
22.92% to 58.33% in a challenging subset of GSM8K, and from 67.91% to 75.40% in
the causal judgment task.

借鉴康德的先验哲学，我们提出了 UPAR 提示框架，通过四个阶段（理解、计划、执行、反思）模拟人类认知结构，在提取结构化信息、先前规划解决方案、按计划执行和自我反思方面，显著增强了 LLM 推理的可解释性和准确性。此外，我们的工作为现有提示技术建立了认识论基础，为这些方法的系统整合提供可能。在 GSM8K 的具有挑战性的子集中，与 COT 基线的 22.92% 相比，我们的方法使准确率提升至 58.33%；在因果判断任务中，准确率从 67.91% 提高到 75.40%。