@inproceedings{krsteski-etal-2026-valid,
    title = "Valid Survey Simulations with Limited Human Data: The Roles of Prompting, Fine-Tuning, and Rectification",
    author = "Krsteski, Stefan  and
      Russo, Giuseppe  and
      Chang, Serina  and
      West, Robert  and
      Gligori{\textbackslash}'c, Kristina",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.498/",
    pages = "10887--10906",
    ISBN = "979-8-89176-390-6",
    abstract = "Surveys provide valuable insights into public opinion and behavior, but their execution is costly and slow. Large language models (LLMs) have been proposed as a scalable, low-cost substitute for human respondents, but their outputs are often biased and yield invalid estimates. We study the interplay between synthesis methods that use LLMs to generate survey responses and rectification methods that debias population estimates, and explore how human responses are best allocated between them. Using two panel surveys with questions on nutrition, politics, and economics, we find that synthesis alone introduces substantial bias (24{--}86{\%}), whereas combining it with rectification reduces bias below 5{\%} and increases effective sample size by up to 14{\%}. Overall, we challenge the common practice of using all human responses for fine-tuning, showing that under a fixed budget, allocating most to rectification results in far more effective estimation."
}