Large language models (LLMs) have demonstrated impressive performance on many tasks. However, to achieve optimal performance, specially designed prompting methods are still needed. These methods either rely on task-specific few-shot examples that require a certain level of domain knowledge, or are designed to be simple but only perform well on a few types of tasks. In this work, we attempt to introduce the concept of generalist prompting, which operates on the design principle of achieving optimal or near-optimal performance on a wide range of tasks while eliminating the need for manual selection and customization of prompts tailored to specific problems. Furthermore, we propose MeMo (Mental Models), an innovative prompting method that is simple-designed yet effectively fulfills the criteria of generalist prompting. MeMo distills the cores of various prompting methods into individual mental models and allows LLMs to autonomously select the most suitable mental models for the problem, achieving or being near to the state-of-the-art results on diverse tasks such as STEM, logical reasoning, and commonsense reasoning in zero-shot settings. We hope that the insights presented herein will stimulate further exploration of generalist prompting methods for LLMs.

翻译：大语言模型在众多任务上展现出令人瞩目的性能。然而，为达到最优效果，仍需采用专门设计的提示方法。这些方法要么依赖需要特定领域知识的任务专属少样本示例，要么追求简化设计但仅能在少数任务类型上表现良好。本研究尝试引入通用提示方法的概念，其设计原则是在广泛任务上实现最优或接近最优性能，同时消除针对具体问题手动选择和定制提示的需求。我们进一步提出MeMo（心理模型）这一创新提示方法，该方法设计简洁却有效满足通用提示方法的标准。MeMo将各类提示方法的核心提炼为独立心理模型，使大语言模型能自主选择最适合问题的心理模型，在零样本设置下于STEM、逻辑推理和常识推理等多样化任务上达到或接近最先进水平。我们希望本文提出的见解能激励对大语言模型通用提示方法的进一步探索。