The Problems of LLM-generated Data in Social Science Research

• Published in: Sociologica (Bologna) Vol. 18; no. 2; pp. 145 - 168
• Main Authors: Rossi, Luca, Harrison, Katherine, Shklovski, Irina
• Format: Journal Article
• Language: English
• Published: Bologna Societa Editrice il Mulino 01.01.2024; University of Bologna
• Subjects: Data; Data collection; Epistemology; Humanities; llm; Methodological problems; Research design; research methods; Research subjects; Social research; social science; Social sciences; synthetic data; research methods; LLM; social science; synthetic data
• ISSN: 1971-8853; 1971-8853
• DOI: 10.6092/issn.1971-8853/19576

Beyond being used as fast and cheap annotators for otherwise complex classification tasks, LLMs have seen a growing adoption for generating synthetic data for social science and design research. Researchers have used LLM-generated data for data augmentation and prototyping, as well as for direct analysis where LLMs acted as proxies for real human subjects. LLM-based synthetic data build on fundamentally different epistemological assumptions than previous synthetically generated data and are justified by a different set of considerations. In this essay, we explore the various ways in which LLMs have been used to generate research data and consider the underlying epistemological (and accompanying methodological) assumptions. We challenge some of the assumptions made about LLM-generated data, and we highlight the main challenges that social sciences and humanities need to address if they want to adopt LLMs as synthetic data generators.