Computational and Text Analysis Methods
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Abstract
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.
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@article{Desai2026Validating,
title = {Validating LLMs in social science: Epistemic threats and emerging norms},
author = {Meera Desai and Dallas Card and Abigail Z. Jacobs},
journal = {arXiv (Cornell University)},
year = {2026},
doi = {10.48550/arxiv.2607.07915},
url = {https://doi.org/10.48550/arxiv.2607.07915}
}FAQ
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