@inproceedings{suh-etal-2025-language,
title = "Language Model Fine-Tuning on Scaled Survey Data for Predicting Distributions of Public Opinions",
author = "Suh, Joseph and
Jahanparast, Erfan and
Moon, Suhong and
Kang, Minwoo and
Chang, Serina",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.1028/",
doi = "10.18653/v1/2025.acl-long.1028",
pages = "21147--21170",
ISBN = "979-8-89176-251-0",
abstract = "Large language models (LLMs) present novel opportunities in public opinion research by predicting survey responses in advance during the early stages of survey design. Prior methods steer LLMs via descriptions of subpopulations as LLMs' input prompt, yet such prompt engineering approaches have struggled to faithfully predict the distribution of survey responses from human subjects. In this work, we propose directly fine-tuning LLMs to predict response distributions by leveraging unique structural characteristics of survey data. To enable fine-tuning, we curate SubPOP, a significantly scaled dataset of 3,362 questions and 70K subpopulation-response pairs from well-established public opinion surveys. We show that fine-tuning on SubPOP greatly improves the match between LLM predictions and human responses across various subpopulations, reducing the LLM-human gap by up to 46{\%} compared to baselines, and achieves strong generalization to unseen surveys and subpopulations. Our findings highlight the potential of survey-based fine-tuning to improve opinion prediction for diverse, real-world subpopulations and therefore enable more efficient survey designs."
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%0 Conference Proceedings
%T Language Model Fine-Tuning on Scaled Survey Data for Predicting Distributions of Public Opinions
%A Suh, Joseph
%A Jahanparast, Erfan
%A Moon, Suhong
%A Kang, Minwoo
%A Chang, Serina
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F suh-etal-2025-language
%X Large language models (LLMs) present novel opportunities in public opinion research by predicting survey responses in advance during the early stages of survey design. Prior methods steer LLMs via descriptions of subpopulations as LLMs’ input prompt, yet such prompt engineering approaches have struggled to faithfully predict the distribution of survey responses from human subjects. In this work, we propose directly fine-tuning LLMs to predict response distributions by leveraging unique structural characteristics of survey data. To enable fine-tuning, we curate SubPOP, a significantly scaled dataset of 3,362 questions and 70K subpopulation-response pairs from well-established public opinion surveys. We show that fine-tuning on SubPOP greatly improves the match between LLM predictions and human responses across various subpopulations, reducing the LLM-human gap by up to 46% compared to baselines, and achieves strong generalization to unseen surveys and subpopulations. Our findings highlight the potential of survey-based fine-tuning to improve opinion prediction for diverse, real-world subpopulations and therefore enable more efficient survey designs.
%R 10.18653/v1/2025.acl-long.1028
%U https://aclanthology.org/2025.acl-long.1028/
%U https://doi.org/10.18653/v1/2025.acl-long.1028
%P 21147-21170
Markdown (Informal)
[Language Model Fine-Tuning on Scaled Survey Data for Predicting Distributions of Public Opinions](https://aclanthology.org/2025.acl-long.1028/) (Suh et al., ACL 2025)
ACL