How Many Human Survey Respondents is a Large Language Model Worth? An Uncertainty Quantification Perspective
arXiv:2502.17773v5 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used to simulate survey responses, but synthetic data can be misaligned with the human population, leading to unreliable inference. We develop a general framework that converts LLM-simulated responses into reliable confidence sets for population parameters of human responses, quantifying the uncertainty induced by the human-LLM misalignment. The key design choice is the number of simulated responses: too many produce overly narrow sets with poor coverage, while too few yield overly wide and
The proliferation of LLMs capable of simulating human responses necessitates robust methods for evaluating the reliability and validity of synthetic data in empirical research.
This development allows for quantifying the uncertainty in LLM-generated survey data, enabling more reliable application of AI in social science research and market analysis, while also highlighting the inherent limitations.
The ability to formally convert LLM-simulated responses into reliable confidence sets means researchers can now apply LLMs for survey data generation with a quantifiable understanding of their accuracy and misalignment.
- · AI researchers
- · Social scientists
- · Market research firms
- · Survey data providers with poor validation protocols
- · Firms relying solely on unvalidated synthetic data
Increased confidence and adoption of LLMs for generating synthetic data in research and business applications.
Demand for specialized tools and libraries that implement uncertainty quantification for AI-generated data.
Ethical and regulatory discussions around the appropriate disclosure and use of LLM-simulated data in public discourse and policy-making.
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Read at arXiv cs.LG