Modern large language models (LLMs), such as ChatGPT, exhibit a remarkable
capacity for role-playing, enabling them to embody not only human characters
but also non-human entities like a Linux terminal. This versatility allows them
to simulate complex human-like interactions and behaviors within various
contexts, as well as to emulate specific objects or systems. While these
capabilities have enhanced user engagement and introduced novel modes of
interaction, the influence of role-playing on LLMs' reasoning abilities remains
underexplored. In this study, we introduce a strategically designed role-play
prompting methodology and assess its performance under the zero-shot setting
across twelve diverse reasoning benchmarks, encompassing arithmetic,
commonsense reasoning, symbolic reasoning, and more. Leveraging models such as
ChatGPT and Llama 2, our empirical results illustrate that role-play prompting
consistently surpasses the standard zero-shot approach across most datasets.
Notably, accuracy on AQuA rises from 53.5% to 63.8%, and on Last Letter from
23.8% to 84.2%. Beyond enhancing contextual understanding, we posit that
role-play prompting serves as an implicit Chain-of-Thought (CoT) trigger,
thereby improving the quality of reasoning. By comparing our approach with the
Zero-Shot-CoT technique, which prompts the model to "think step by step", we
further demonstrate that role-play prompting can generate a more effective CoT.
This highlights its potential to augment the reasoning capabilities of LLMs.

通过角色扮演提示方法，在各种推理基准测试中展示了大型语言模型 (LLMs) 的推理能力的提升。