Stanford U & Open AI’s Meta-Prompting Elevates Language Model Performance, Surpassing Standard Prompting by 17%

The latest advancements in language models (LMs), exemplified by GPT-4 (OpenAI, 2023), PaLM (Anil et al., 2023), and LLaMa (Touvron et al., 2023), have demonstrated remarkable capabilities in natural language processing and generation tasks. However, these cutting-edge models occasionally produce responses that are inaccurate, misleading, or conflicting, underscoring the need to improve the accuracy and robustness of their outputs.

In a new paper Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffolding, the team introduces meta-prompting. This innovative scaffolding approach proves to be highly effective, surpassing standard prompting by 17.1%, expert (dynamic) prompting by 17.3%, and multi-persona prompting by 15.2%.

Meta-prompting builds upon and combines various prompting ideas proposed in recent studies, incorporating high-level planning and decision-making, dynamic persona assignment, multi-agent debating, self-debugging, and self-reflection. Notably, its task-agnostic nature sets it apart from traditional scaffolding methods, as it employs a consistent set of high-level instructions across different tasks and inputs. This universality is especially advantageous for users who may find it cumbersome to provide detailed examples or specific guidance for each unique task.

At the core of meta-prompting is its shallow hierarchical configuration, where a singular model, known as the “Meta Model,” assumes the role of the principal authority. Conceptually, domain-specific experts within this framework can take various forms, such as fine-tuned LMs tailored for specific tasks, specialized APIs handling domain-related inquiries, or computational tools like calculators. Despite their diverse functionalities, these experts operate under the unified supervision of the Meta Model.

Conceptually, a domain-specific expert within their framework can take diverse forms, such as a finetuned LM tailored to perform a particular task, a specialized API equipped to handle specific domain-related inquiries, or even computational tools like calculators. These experts, despite their varying functionalities, are directed and unified under the supervision of the Meta Model.

In extensive experiments primarily utilizing GPT-4 as the foundational LM, the researchers compare the efficacy of meta-prompting against other task-agnostic scaffolding methods. The results indicate that meta-prompting consistently outperforms standard prompting by 17.1%, expert (dynamic) prompting by 17.3%, and multi-persona prompting by 15.2%.

In summary, this research establishes meta-prompting as a straightforward yet potent scaffolding technique that significantly enhances the performance of language models. Empirical evidence demonstrates that meta-prompting not only improves overall performance but also often achieves state-of-the-art results in a task-agnostic manner across a diverse array of tasks.

The paper Meta-Prompting: Enhancing Language Models with Task-Agnostic ScaffoldingarXiv.

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Author: Hecate He | Editor: Chain Zhang

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