  Modern large language models (LLMs) exhibit a remarkable capacity for
role-playing, enabling them to embody not only human characters but also
non-human entities. 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. Our empirical results illustrate that role-play prompting
consistently surpasses the standard zero-shot approach across most datasets.
Notably, in experiments conducted using ChatGPT, accuracy on AQuA rises from
53.5% to 63.8%, and on Last Letter from 23.8% to 84.2%.Upon further comparison
with the Zero-Shot-CoT technique, which prompts the model to "think step by
step", our study demonstrates that role-play prompting acts as a more effective
trigger for the CoT process. This highlights its potential to augment the
reasoning capabilities of LLMs. We release our code at
https://github.com/NKU-HLT/Role-Play-Prompting.
