Role-Play Paradox in Large Language Models: Reasoning Performance Gains and Ethical Dilemmas

Understand

Role-play in large language models (LLMs) enhances their ability to generate contextually relevant and high-quality responses by simulating diverse cognitive perspectives.

• However, our study identifies significant risks associated with this technique.
• First, we demonstrate that autotuning, a method used to auto-select models' roles based on the question, can lead to the generation of harmful outputs, even when the model is tasked with adopting neutral roles.
• Second, we investigate how different roles affect the likelihood of generating biased or harmful content.