Meta Prompting for AI Systems (2311.11482v6)

Abstract: In this work, we present a comprehensive study of Meta Prompting (MP), an innovative technique reshaping the utilization of LLMs (LMs) and AI systems in problem-solving and data interaction. Grounded in type theory and category theory, Meta Prompting emphasizes the structure and syntax of information over traditional content-centric methods. The paper explores the formal definitions of Meta Prompting, sets it apart from few-shot prompting, and underlines its effectiveness in various AI applications. A key focus is applying Meta Prompting for complex reasoning tasks, showing how it effectively deconstructs intricate problems into simpler sub-problems, enhancing token efficiency, and enabling more equitable problem-solving comparisons, especially against few-shot prompting methods. Additionally, the paper introduces Meta Prompting for prompting tasks, allowing LLMs to self-generate new prompts in a recursive, metaprogramming-like manner. Empirical experiments, including using a Qwen-72B base LLM equipped with meta prompt without instruction-tuning to solve MATH problems with accuracy at 46.3%, which surpass the supervised fine-tuned counterpart trained with extensive mathematical QA instruction pairs and even the initial version of GPT-4, solving GSM8K problems with 83.5% accuracy with zero-shot meta-prompted Qwen-72B base LLM, and solving the Game of 24 tasks with a 100% success rate using GPT-4, demonstrate the meta prompting's efficacy in achieving high accuracy and efficiency, showcasing Meta Prompting's transformative impact on AI problem-solving The code is available at https://github.com/meta-prompting/meta-prompting.

Meta Prompting for AI Systems: A New Approach to Problem Structuring

The paper "Meta Prompting for AI Systems" introduces an innovative approach to leveraging LLMs (LMs) by emphasizing the structural and syntactic facets of prompting—dubbed Meta Prompting. This methodology diverges from traditional content-centric techniques and instead draws on principles from type theory and category theory to provide a formalized framework for deconstructing complex problems, thus enhancing reasoning tasks and data interaction capabilities.

Key Contributions

1. Meta Prompting Definition and Framework: The authors propose Meta Prompting (MP) as a method for utilizing structured prompts to address intricate tasks efficiently. This approach leverages category and type theories to abstract tasks into more systematic representations using functors. The paper contrasts Meta Prompting with few-shot prompting, illustrating its superior ability to guide LLMs in logical and step-by-step reasoning processes without content-specific examples.
2. Applications and Empirical Performance: Meta Prompting's efficacy is demonstrated across various AI applications, particularly in complex reasoning tasks like mathematical problem-solving. The paper presents impressive empirical results, such as a zero-shot meta-prompted Qwen-72B model achieving 46.3% accuracy on the MATH dataset, outperforming several fine-tuned models and earlier iterations of GPT-4. Similarly, the model achieved 83.5% accuracy on the GSM8K dataset, validating the practical benefits of this structural approach.
3. Recursive Meta Prompting for Prompt Generation: The research extends the idea of Meta Prompting to Recursive Meta Prompting (RMP), wherein LLMs autonomously generate new prompts in a recursive manner. This is particularly useful for enhancing model autonomy and adaptability, enabling LLMs to dynamically create and refine prompts based on evolving task requirements.
4. Integration with Symbolic and Physical Environments: The approach also emphasizes the potential of Meta Prompting when interfaced with symbolic systems and code environments. By employing structured prompts, AI systems can interact more effectively with symbols and manage computational tasks, further enhancing their utility in domains that demand high precision, such as code interpretation and physical applications.
5. Experimentation and Performance Metrics: Practical validations of Meta Prompting include its application to the Game of 24 task, where the method attained a 100% success rate. It significantly economizes on token usage and establishes fair comparisons with minimal reliance on content-based training, emphasizing its efficiency and fairness over traditional few-shot models.

Implications and Future Directions

Theoretical implications of Meta Prompting suggest a shift toward more structured, syntax-driven AI models, which could profoundly impact cognitive science research by mirroring human-like reasoning processes. Practically, MP offers significant improvements in token efficiency and reasoning transparency.

The future scope of Meta Prompting could encompass its integration into multi-modal settings, enhancing its applicability across different data types such as text, images, and audio. This evolution may see the framework being adapted to accommodate the complex, dynamic nature of real-world data, thereby broadening the horizons for AI applications.

In conclusion, the paper presents a novel, structured approach to enhancing AI reasoning capabilities through Meta Prompting, offering a robust framework that combines the strengths of theoretical formalism with practical efficiency. This marks a significant advance in aligning LLMs closer to human-like reasoning and problem-solving proficiency.