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.

大语言模型 (LLM) 在许多任务上展现出了令人印象深刻的性能。然而，为了实现最佳性能，仍然需要特殊设计的提示方法。在这项工作中，我们试图引入通用提示的概念，它在设计原则上旨在在广泛的任务范围内实现最佳或接近最佳的性能，同时消除了手动选择和定制面向特定问题的提示的需求。此外，我们提出了一种创新的提示方法 MeMo（心智模型），它设计简单但有效地满足通用提示的要求。MeMo 将各种提示方法的要点提炼成个别的心智模型，并允许 LLMs 自主选择最合适的心智模型来解决问题，在零样本设置下在 STEM、逻辑推理和常识推理等多种任务上达到或接近于最新的研究结果。我们希望本文所提出的见解能够激发更多对 LLMs 的通用提示方法的探索。