Should you use LLMs to simulate opinions? Quality checks for early-stage deliberation

• URL: http://arxiv.org/abs/2504.08954v3
• Date: Fri, 08 Aug 2025 20:37:08 GMT
• Title: Should you use LLMs to simulate opinions? Quality checks for early-stage deliberation
• Authors: Terrence Neumann, Maria De-Arteaga, Sina Fazelpour,
• Abstract summary: We introduce a quality control assessment designed to test the viability of LLM-simulated opinions on Likert-scale tasks.<n>This assessment comprises two key tests: emphlogical consistency and emphalignment with stakeholder expectations.<n>None of the models or methods pass the full assessment, revealing several failure modes.
• Score: 5.4260854377259315
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The emergent capabilities of large language models (LLMs) have prompted interest in using them as surrogates for human subjects in opinion surveys. However, prior evaluations of LLM-based opinion simulation have relied heavily on costly, domain-specific survey data, and mixed empirical results leave their reliability in question. To enable cost-effective, early-stage evaluation, we introduce a quality control assessment designed to test the viability of LLM-simulated opinions on Likert-scale tasks without requiring large-scale human data for validation. This assessment comprises two key tests: \emph{logical consistency} and \emph{alignment with stakeholder expectations}, offering a low-cost, domain-adaptable validation tool. We apply our quality control assessment to an opinion simulation task relevant to AI-assisted content moderation and fact-checking workflows -- a socially impactful use case -- and evaluate seven LLMs using a baseline prompt engineering method (backstory prompting), as well as fine-tuning and in-context learning variants. None of the models or methods pass the full assessment, revealing several failure modes. We conclude with a discussion of the risk management implications and release \texttt{TopicMisinfo}, a benchmark dataset with paired human and LLM annotations simulated by various models and approaches, to support future research.

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