Validating LLMs in social science: Epistemic threats and emerging norms

2026-07-08

AI Summary

Large language models (LLMs) are increasingly employed as measurement instruments in the social sciences, yet their susceptibility to bias, hallucination, and context sensitivity—coupled with the absence of standardized validation protocols—poses significant threats to research validity. This study systematically reviews empirical applications of LLM-based measurement across eight top-tier journals, revealing through bibliometric and methodological analysis that current validation practices are generally weak and inconsistent. In response, the paper proposes a complementary validation framework that integrates multidimensional assessment strategies to enhance the reliability and transparency of LLM measurements and to foster the development of shared methodological standards within the field.

Abstract

Large language models (LLMs) are reshaping social science methodology. Researchers increasingly prompt language models to generate quantitative measurements of social concepts, for example labeling data or simulating survey responses. Yet LLMs pose methodological challenges including bias, hallucination, and brittleness across contexts, with unclear threats to validity. Standard practices and norms for addressing these challenges are still emerging. We collect and systematically analyze validation practices in a comprehensive corpus of papers from eight flagship social science journals that use LLMs as measurement instruments. We find that LLM-generated measurements frequently play a central role in empirical analyses, yet validation practices are inconsistent and limited. We outline complementary strategies for more robust validation, pointing toward better norms and standards around the use of LLMs in social science.

Problem

• large language models
• social science
• measurement validity
• methodological challenges
• validation practices

Innovation

• LLM validation
• social science methodology
• measurement reliability
• epistemic threats
• emerging norms