More Parameters Than Populations: A Systematic Literature Review of Large Language Models within Survey Research

• URL: http://arxiv.org/abs/2509.03391v1
• Date: Wed, 03 Sep 2025 15:15:31 GMT
• Title: More Parameters Than Populations: A Systematic Literature Review of Large Language Models within Survey Research
• Authors: Trent D. Buskirk, Florian Keusch, Leah von der Heyde, Adam Eck,
• Abstract summary: Large Language Models (LLMs) bring new technological challenges and prerequisites in order to fully harness their potential.<n>We report work-in-progress on a systematic literature review based on keyword searches from multiple large-scale databases.<n>We discuss selected examples of potential use cases for LLMs as well as its pitfalls based on examples from existing literature.
• Score: 0.7699714865575188
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Survey research has a long-standing history of being a human-powered field, but one that embraces various technologies for the collection, processing, and analysis of various behavioral, political, and social outcomes of interest, among others. At the same time, Large Language Models (LLMs) bring new technological challenges and prerequisites in order to fully harness their potential. In this paper, we report work-in-progress on a systematic literature review based on keyword searches from multiple large-scale databases as well as citation networks that assesses how LLMs are currently being applied within the survey research process. We synthesize and organize our findings according to the survey research process to include examples of LLM usage across three broad phases: pre-data collection, data collection, and post-data collection. We discuss selected examples of potential use cases for LLMs as well as its pitfalls based on examples from existing literature. Considering survey research has rich experience and history regarding data quality, we discuss some opportunities and describe future outlooks for survey research to contribute to the continued development and refinement of LLMs.

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