  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. Without using few-shot examples or external tools,
UPAR significantly outperforms existing prompting methods on SCIBENCH, a
challenging dataset containing collegiate-level mathematics, chemistry, and
physics scientific problems.
