**Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffolding** **Authors:** Mirac Suzgun **Abstract:** Meta-prompting is a novel scaffolding technique designed to enhance the functionality of language models (LMs). This approach transforms a single LM into a multi-faceted conductor, capable of 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. The LM acts as the conductor, ensuring seamless communication and effective integration of the outputs from these expert models. It also employs critical thinking and robust verification processes to refine and authenticate the final result. This collaborative prompting approach empowers a single LM to act as both 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 simplifies user interaction by eliminating the need for detailed, task-specific instructions. Additionally, the integration of external tools, such as a Python interpreter, broadens the applicability and utility of the meta-prompting framework. Through rigorous experimentation with GPT-4, the study demonstrates that meta-prompting, particularly when combined with a Python interpreter, surpasses conventional scaffolding methods in terms of accuracy and robustness. **Key Contributions:** 1. **Meta-Prompting Technique:** A task-agnostic scaffolding system that leverages a single LM to coordinate and execute multiple independent inquiries, enhancing overall performance. 2. **Task-Agnostic Nature:** The technique employs the same set of high-level instructions across various tasks, simplifying user interaction. 3. **Python Interpreter Integration:** Enhances the framework's versatility and utility by allowing dynamic and comprehensive application of the technique. **Experimental Setup:** - **Baselines:** Standard prompting, zero-shot CoI prompting, expert prompting, and multipersona prompting. - **Datasets and Tasks:** Various tasks including Game of 24, Checkmate-in-One, Python Programming Puzzles, Multilingual Grade School Math, and Shakespearean Sonnet Writing. - **Evaluation Metrics:** Exact Match (EM), Soft Match (SM), and Functionally Correct (FC). **Results:** - Meta-prompting outperforms standard prompting by 17.1%, expert (dynamic) prompting by 17.3%, and multipersona prompting by 15.2%. - The integration of a Python interpreter significantly improves performance in many tasks, particularly in algorithmic challenges. **Discussion:** - **Overall Performance:** Meta-prompting demonstrates superior effectiveness across various tasks, especially in heuristic and iterative problem-solving. - **Zero-Shot Decomposition, Error Detection, and Aggregation:** The framework leverages specialized knowledge and implicit**Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffolding** **Authors:** Mirac Suzgun **Abstract:** Meta-prompting is a novel scaffolding technique designed to enhance the functionality of language models (LMs). This approach transforms a single LM into a multi-faceted conductor, capable of 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. The LM acts as the conductor, ensuring seamless communication and effective integration of the outputs from these expert models. It also employs critical thinking and robust verification processes to refine and authenticate the final result. This collaborative prompting approach empowers a single LM to act as both 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 simplifies user interaction by eliminating the need for detailed, task-specific instructions. Additionally, the integration of external tools, such as a Python interpreter, broadens the applicability and utility of the meta-prompting framework. Through rigorous experimentation with GPT-4, the study demonstrates that meta-prompting, particularly when combined with a Python interpreter, surpasses conventional scaffolding methods in terms of accuracy and robustness. **Key Contributions:** 1. **Meta-Prompting Technique:** A task-agnostic scaffolding system that leverages a single LM to coordinate and execute multiple independent inquiries, enhancing overall performance. 2. **Task-Agnostic Nature:** The technique employs the same set of high-level instructions across various tasks, simplifying user interaction. 3. **Python Interpreter Integration:** Enhances the framework's versatility and utility by allowing dynamic and comprehensive application of the technique. **Experimental Setup:** - **Baselines:** Standard prompting, zero-shot CoI prompting, expert prompting, and multipersona prompting. - **Datasets and Tasks:** Various tasks including Game of 24, Checkmate-in-One, Python Programming Puzzles, Multilingual Grade School Math, and Shakespearean Sonnet Writing. - **Evaluation Metrics:** Exact Match (EM), Soft Match (SM), and Functionally Correct (FC). **Results:** - Meta-prompting outperforms standard prompting by 17.1%, expert (dynamic) prompting by 17.3%, and multipersona prompting by 15.2%. - The integration of a Python interpreter significantly improves performance in many tasks, particularly in algorithmic challenges. **Discussion:** - **Overall Performance:** Meta-prompting demonstrates superior effectiveness across various tasks, especially in heuristic and iterative problem-solving. - **Zero-Shot Decomposition, Error Detection, and Aggregation:** The framework leverages specialized knowledge and implicit