Certain statistical models are capable of interpreting input strings as instructions, or prompts, and carry out tasks based on them. Many approaches to prompting and pre-training these models involve the automated generation of these prompts. We call these approaches meta-prompting, or prompting to obtain prompts. We propose a theoretical framework based on category theory to generalize and describe them. This framework is flexible enough to account for LLM stochasticity; and allows us to obtain formal results around task agnosticity and equivalence of various meta-prompting approaches. We experiment with meta-prompting in two active areas of model research: creativity and ideation. We find that user preference favors (p < 0.01) the prompts generated under meta-prompting, as well as their corresponding outputs, over a series of hardcoded baseline prompts that include the original task prompt. Using our framework, we argue that meta-prompting is more effective than basic prompting at generating desirable outputs.

翻译：某些统计模型能够将输入字符串解释为指令或提示，并据此执行任务。许多提示方法和预训练方法涉及这些提示的自动生成。我们将这些方法称为元提示，即通过提示获取提示。我们提出了一个基于范畴论的理论框架来概括和描述这些方法。该框架具有足够的灵活性以解释大语言模型的随机性，并使我们能够获得关于任务无关性及各类元提示方法等价性的形式化结果。我们在模型研究的两个活跃领域——创造力与构思——中进行了元提示实验。研究发现，用户更偏好（p < 0.01）在元提示下生成的提示及其对应输出，这优于包含原始任务提示在内的一系列硬编码基线提示。利用该框架，我们论证了元提示在生成理想输出方面比基础提示更有效。