This paper presents an in-depth exploration of Meta Prompting, a novel technique that revolutionizes the way large language models (LLMs), multi-modal foundation models, and AI systems approach problem-solving and data interpretation. Meta Prompting, rooted in type theory and category theory, prioritizes the structure and syntax of information, providing a unique framework that transcends traditional content-focused methods. We delve into the formal definitions of Meta Prompting, contrasting it with Few-Shot Prompting, and highlight its applicability and superiority in various AI applications. Key to this exploration is the expansion of Meta Prompting into the realm of complex reasoning. Here, we demonstrate how this technique adeptly breaks down intricate problems into manageable sub-problems, facilitating a step-by-step, detailed approach to problem-solving. This method proves especially advantageous in terms of token efficiency and offering a fair comparison in problem-solving scenarios, standing out against few-shot example approaches. Furthermore, the paper breaks new ground by extending Meta Prompting into multi-modal foundation model settings. This extension addresses the integration of diverse data types, such as images, audio, and video, within the structured framework of Meta Prompting, highlighting both the challenges and the vast potential of this approach in handling complex, multi-faceted data (The code is available at https://github.com/meta-prompting/meta-prompting).

翻译：本文深入探讨了元提示这一创新技术，该技术彻底改变了大型语言模型、多模态基础模型及人工智能系统在问题求解与数据解读中的运作方式。根植于类型论与范畴论的元提示方法，优先关注信息的结构与语法，构建出超越传统内容导向方法的独特框架。通过对比少样本提示方法，我们详细阐释了元提示的形式化定义，并揭示其在各类人工智能应用中的适用性与优越性。本研究的核心在于将元提示扩展至复杂推理领域：我们论证了该技术如何将复杂问题有效分解为可管理的子问题，从而促进逐步推演的精细化问题求解。相较少样本示例方法，该方法在令牌效率与公平比较场景中展现出显著优势。此外，本文首次将元提示拓展至多模态基础模型场景，探讨了将图像、音频、视频等异构数据类型整合至元提示结构化框架的实现路径，既揭示了该方案在应对复杂多源数据时的挑战，也彰显了其广阔应用前景（代码详见：https://github.com/meta-prompting/meta-prompting）。