  Recent advancements in Large Language Models empower them to follow freeform
instructions, including imitating generic or specific demographic personas in
conversations. We define generic personas to represent demographic groups, such
as "an Asian person", whereas specific personas may take the form of specific
popular Asian names like "Yumi". While the adoption of personas enriches user
experiences by making dialogue systems more engaging and approachable, it also
casts a shadow of potential risk by exacerbating social biases within model
responses, thereby causing societal harm through interactions with users. In
this paper, we systematically study "persona biases", which we define to be the
sensitivity of dialogue models' harmful behaviors contingent upon the personas
they adopt. We categorize persona biases into biases in harmful expression and
harmful agreement, and establish a comprehensive evaluation framework to
measure persona biases in five aspects: Offensiveness, Toxic Continuation,
Regard, Stereotype Agreement, and Toxic Agreement. Additionally, we propose to
investigate persona biases by experimenting with UNIVERSALPERSONA, a
systematically constructed persona dataset encompassing various types of both
generic and specific model personas. Through benchmarking on four different
models -- including Blender, ChatGPT, Alpaca, and Vicuna -- our study uncovers
significant persona biases in dialogue systems. Our findings also underscore
the pressing need to revisit the use of personas in dialogue agents to ensure
safe application.
