ChatGPT vs Social Surveys: Probing Objective and Subjective Silicon Population

TL;DR

This study investigates how GPT-generated responses compare to human social survey data, revealing demographic biases, inconsistent attitude estimates, and more deterministic answers, raising concerns about using LLMs for social research.

Contribution

It provides a detailed analysis of GPT's demographic and attitudinal response patterns, highlighting biases and differences from human respondents in social survey contexts.

Findings

GPT's demographic distribution matches 2020 U.S. population for gender and age

GPT overestimates Black population and higher education levels

GPT responses are more deterministic and normally distributed than human responses

Abstract

Recent discussions about Large Language Models (LLMs) indicate that they have the potential to simulate human responses in social surveys and generate reliable predictions, such as those found in political polls. However, the existing findings are highly inconsistent, leaving us uncertain about the population characteristics of data generated by LLMs. In this paper, we employ repeated random sampling to create sampling distributions that identify the population parameters of silicon samples generated by GPT. Our findings show that GPT's demographic distribution aligns with the 2020 U.S. population in terms of gender and average age. However, GPT significantly overestimates the representation of the Black population and individuals with higher levels of education, even when it possesses accurate knowledge. Furthermore, GPT's point estimates for attitudinal scores are highly inconsistent and show no clear inclination toward any particular ideology. The sample response distributions exhibit a normal pattern that diverges significantly from those of human respondents. Consistent with previous studies, we find that GPT's answers are more deterministic than those of humans. We conclude by discussing the concerning implications of this biased and deterministic silicon population for making inferences about real-world populations.