Can Large Language Models Capture Public Opinion about Global Warming? An Empirical Assessment of Algorithmic Fidelity and Bias

Understand

Large language models (LLMs) have demonstrated their potential in social science research by emulating human perceptions and behaviors, a concept referred to as algorithmic fidelity.

• This study assesses the algorithmic fidelity and bias of LLMs by utilizing two nationally representative climate change surveys.
• The LLMs were conditioned on demographics and/or psychological covariates to simulate survey responses.
• The findings indicate that LLMs can effectively capture presidential voting behaviors but encounter challenges in accurately representing global warming perspectives when relevant covariates are not included.