Random Silicon Sampling: Simulating Human Sub-Population Opinion Using a Large Language Model Based on Group-Level Demographic Information

Understand

Large language models exhibit societal biases associated with demographic information, including race, gender, and others.

• Endowing such language models with personalities based on demographic data can enable generating opinions that align with those of humans.
• Building on this idea, we propose "random silicon sampling," a method to emulate the opinions of the human population sub-group.
• Our study analyzed 1) a language model that generates the survey responses that correspond with a human group based solely on its demographic distribution and 2) the applicability of our methodology across various demographic subgroups and thematic questions.