Your Large Language Model is Secretly a Fairness Proponent and You
  Should Prompt it Like One
Tianlin Li Xiaoyu Zhang Chao Du Tianyu Pang Qian Liu Qing Guo Chao Shen Yang Liu
The widespread adoption of large language models (LLMs) underscores the
urgent need to ensure their fairness. However, LLMs frequently present dominant
viewpoints while ignoring alternative perspectives from minority parties,
resulting in potential biases. We hypothesize that these fairness-violating
behaviors occur because LLMs express their viewpoints using a human personality
that represents the majority of training data. In response to this, we validate
that prompting LLMs with specific roles can allow LLMs to express diverse
viewpoints. Building on this insight and observation, we develop FairThinking,
a pipeline designed to automatically generate roles that enable LLMs to
articulate diverse perspectives for fair expressions. To evaluate FairThinking,
we create a dataset with a thousand items covering three fairness-related
topics and conduct experiments on GPT-3.5, GPT-4, Llama2, and Mistral to
demonstrate its superior performance.