Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffolding
Authors: name: Mirac Suzgun, name: Adam Tauman Kalai

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%.