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LLMs simulate survey respondents, offering new social science research tools

Researchers have developed a new benchmark called LLM-S^3 to evaluate how well large language models can simulate human respondents in surveys. The benchmark includes 11 real-world datasets across various sociological domains. Experiments using GPT-3.5/4 Turbo and LLaMA 3.0/3.1-8B showed consistent performance trends and highlighted how prompt design impacts simulation accuracy. AI

Summary written by gemini-2.5-flash-lite from 1 source. How we write summaries →

IMPACT Introduces a new benchmark for evaluating LLM simulation capabilities, potentially improving data collection methods in social sciences.

RANK_REASON The cluster contains an academic paper introducing a new benchmark for evaluating LLMs in survey simulation.

Read on arXiv cs.AI →

COVERAGE [1]

  1. arXiv cs.AI TIER_1 · Jianpeng Zhao, Chenyu Yuan, Weiming Luo, Haoling Xie, Guangwei Zhang, Steven Jige Quan, Zixuan Yuan, Pengyang Wang, Denghui Zhang ·

    Large Language Models as Virtual Survey Respondents: Evaluating Sociodemographic Response Generation

    arXiv:2509.06337v2 Announce Type: replace Abstract: Questionnaire-based surveys are foundational to social science research and public policymaking, yet traditional survey methods remain costly, time-consuming, and often limited in scale. Although prior work has explored large la…