arxiv preprint - UPAR: A Kantian-Inspired Prompting Framework for Enhancing Large Language Model Capabilities

In this episode, we discuss UPAR: A Kantian-Inspired Prompting Framework for Enhancing Large Language Model Capabilities by Hejia Geng, Boxun Xu, Peng Li. The paper introduces the UPAR framework for Large Language Models (LLMs) to enhance their inferential abilities by structuring their processes similar to human cognition. UPAR includes four stages: Understand, Plan, Act, and Reflect, which improve the models' explainability and accuracy. The method increases GPT-4's accuracy dramatically on complex problem sets and outperforms existing techniques without relying on few-shot learning or external tools.