We introduce meta-prompting, an effective scaffolding technique designed to
enhance the functionality of language models (LMs). This approach transforms a
single LM into a multi-faceted conductor, adept at managing and integrating
multiple independent LM queries. By employing high-level instructions,
meta-prompting guides the LM to break down complex tasks into smaller, more
manageable subtasks. These subtasks are then handled by distinct "expert"
instances of the same LM, each operating under specific, tailored instructions.
Central to this process is the LM itself, in its role as the conductor, which
ensures seamless communication and effective integration of the outputs from
these expert models. It additionally employs its inherent critical thinking and
robust verification processes to refine and authenticate the end result. This
collaborative prompting approach empowers a single LM to simultaneously act as
a comprehensive orchestrator and a panel of diverse experts, significantly
enhancing its performance across a wide array of tasks. The zero-shot,
task-agnostic nature of meta-prompting greatly simplifies user interaction by
obviating the need for detailed, task-specific instructions. Furthermore, our
research demonstrates the seamless integration of external tools, such as a
Python interpreter, into the meta-prompting framework, thereby broadening its
applicability and utility. Through rigorous experimentation with GPT-4, we
establish the superiority of meta-prompting over conventional scaffolding
methods: When averaged across all tasks, including the Game of 24,
Checkmate-in-One, and Python Programming Puzzles, meta-prompting, augmented
with a Python interpreter functionality, surpasses standard prompting by 17.1%,
expert (dynamic) prompting by 17.3%, and multipersona prompting by 15.2%.

通过引入元提示技术，将单个语言模型转变为多面手指挥者，通过高级指示将复杂任务分解为可管理的子任务，从而提高语言模型的综合性能。元提示方法的任务无关性大大简化了用户交互，而与 Python 解释器等外部工具的无缝集成扩展了其适用性和实用性。在 GPT-4 上进行的实验表明，相比传统的支架方法，使用元提示技术在所有任务上平均效果提高了 17.1%，动态支架方法提高了 17.3%，多身份支架方法提高了 15.2%。