title: Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffolding
summary: 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%.
paper_authors._id: 65b07321403a23a2fd92b66a
paper_authors.name: Mirac Suzgun
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-01-24T02:18:16.281Z
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paper_authors.avatarUrl: /avatars/3c847a33ea10b630830e5a95b5fd38bc.svg
paper_authors.fullname: Mirac Suzgun
paper_authors.type: user
paper_authors.user: turingmachine
paper_authors._id: 65b07321403a23a2fd92b66b
paper_authors.name: Adam Tauman Kalai
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2024-01-24T02:17:05.516Z
paper_authors._id: 64e3b46d618cd90997e9d342
paper_authors.avatarUrl: /avatars/692aa11b62ac880833a8a183018bec70.svg
paper_authors.fullname: Adam Tauman Kalai
paper_authors.type: user
paper_authors.user: adamology
paper_title: Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffolding
paper_summary: 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%.
paper_ai_keywords: meta-prompting language models (LMs) high-level instructions subtasks expert instances critical thinking robust verification processes result-shot task-agnostic Python interpreter GPT-4 standard prompting expert (dynamic) prompting multipersona prompting