@inproceedings{wu-etal-2026-demographic,
title = "Demographic Pluralism: Inference-Time Modeling of Pluralistic Human Preference Distributions",
author = "Wu, Meng-Chen and
Chen, Qipin and
Jain, Ansh and
Wood, Tess and
Du, Zhe and
Chin, Si-Chi",
editor = "Xu, Shanshan and
T.Y.S.S., Santosh and
Kurniawan, Kemal and
Peng, Siyao and
Wallbridge, Sarenne and
Koto, Fajri and
Dziri, Nouha and
Plank, Barbara",
booktitle = "Proceedings of the First Workshop on Pluralistic {AI} {NLP}: Diversity-aware, Sociotechnical, Responsible Alignment ({PANDORA} 2026)",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.pandora-1.1/",
pages = "1--34",
ISBN = "979-8-89176-484-2",
abstract = "Large language models (LLMs) are increasingly used in culturally sensitive settings, where alignment requires representing diverse preferences within populations. Yet existing methods model populations at coarse demographic or community levels and overlook within-group variation. We introduce DEMOGRAPHIC PLURALISM, an inference-time framework that estimates population-level opinion distributions without opinion-distribution training data or task-specific fine-tuning by generating multiple perspectives within census-grounded demographic groups. Across four backbones on GlobalOpinionQA and VITAL, it reduces Jensen{--}Shannon distance by 8.4{\%}{--}26.4{\%} over Modular Pluralism. Among weighted, equal-weighted, and inverse-weighted aggregation, equal weighting performs best overall; group-level error also increases with census weight, helping explain weighted aggregation{'}s weaker performance."
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<abstract>Large language models (LLMs) are increasingly used in culturally sensitive settings, where alignment requires representing diverse preferences within populations. Yet existing methods model populations at coarse demographic or community levels and overlook within-group variation. We introduce DEMOGRAPHIC PLURALISM, an inference-time framework that estimates population-level opinion distributions without opinion-distribution training data or task-specific fine-tuning by generating multiple perspectives within census-grounded demographic groups. Across four backbones on GlobalOpinionQA and VITAL, it reduces Jensen–Shannon distance by 8.4%–26.4% over Modular Pluralism. Among weighted, equal-weighted, and inverse-weighted aggregation, equal weighting performs best overall; group-level error also increases with census weight, helping explain weighted aggregation’s weaker performance.</abstract>
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%0 Conference Proceedings
%T Demographic Pluralism: Inference-Time Modeling of Pluralistic Human Preference Distributions
%A Wu, Meng-Chen
%A Chen, Qipin
%A Jain, Ansh
%A Wood, Tess
%A Du, Zhe
%A Chin, Si-Chi
%Y Xu, Shanshan
%Y T.Y.S.S., Santosh
%Y Kurniawan, Kemal
%Y Peng, Siyao
%Y Wallbridge, Sarenne
%Y Koto, Fajri
%Y Dziri, Nouha
%Y Plank, Barbara
%S Proceedings of the First Workshop on Pluralistic AI NLP: Diversity-aware, Sociotechnical, Responsible Alignment (PANDORA 2026)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-484-2
%F wu-etal-2026-demographic
%X Large language models (LLMs) are increasingly used in culturally sensitive settings, where alignment requires representing diverse preferences within populations. Yet existing methods model populations at coarse demographic or community levels and overlook within-group variation. We introduce DEMOGRAPHIC PLURALISM, an inference-time framework that estimates population-level opinion distributions without opinion-distribution training data or task-specific fine-tuning by generating multiple perspectives within census-grounded demographic groups. Across four backbones on GlobalOpinionQA and VITAL, it reduces Jensen–Shannon distance by 8.4%–26.4% over Modular Pluralism. Among weighted, equal-weighted, and inverse-weighted aggregation, equal weighting performs best overall; group-level error also increases with census weight, helping explain weighted aggregation’s weaker performance.
%U https://aclanthology.org/2026.pandora-1.1/
%P 1-34
Markdown (Informal)
[Demographic Pluralism: Inference-Time Modeling of Pluralistic Human Preference Distributions](https://aclanthology.org/2026.pandora-1.1/) (Wu et al., PANDORA 2026)
ACL
- Meng-Chen Wu, Qipin Chen, Ansh Jain, Tess Wood, Zhe Du, and Si-Chi Chin. 2026. Demographic Pluralism: Inference-Time Modeling of Pluralistic Human Preference Distributions. In Proceedings of the First Workshop on Pluralistic AI NLP: Diversity-aware, Sociotechnical, Responsible Alignment (PANDORA 2026), pages 1–34, Budapest, Hungary. Association for Computational Linguistics.