@inproceedings{kambhatla-etal-2026-improving,
title = "Improving the Distributional Alignment of {LLM}s using Supervision",
author = "Kambhatla, Gauri and
Gautam, Sanjana and
Zhang, Angela and
Liu, Alexander and
Srinivasan, Ravi and
Li, Junyi Jessy and
Lease, Matthew",
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://aclanthology.org/2026.acl-long.1583/",
pages = "34277--34305",
ISBN = "979-8-89176-390-6",
abstract = "The ability to accurately align LLMs with diverse population groups on subjective questions would have great value. In this work, we show that adding simple supervision can more consistently improve the alignment of LLM-generated distributions with diverse population groups, as measured across three datasets spanning public health, public opinion, and values and beliefs. Beyond evaluating average alignment, we also report how alignment varies across specific groups. Our broad findings provide insights into the distributional alignment of LLM generations with diverse populations. By conducting evaluation over many LLMs and prompting strategies, we provide a benchmark to stimulate future research."
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%0 Conference Proceedings
%T Improving the Distributional Alignment of LLMs using Supervision
%A Kambhatla, Gauri
%A Gautam, Sanjana
%A Zhang, Angela
%A Liu, Alexander
%A Srinivasan, Ravi
%A Li, Junyi Jessy
%A Lease, Matthew
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F kambhatla-etal-2026-improving
%X The ability to accurately align LLMs with diverse population groups on subjective questions would have great value. In this work, we show that adding simple supervision can more consistently improve the alignment of LLM-generated distributions with diverse population groups, as measured across three datasets spanning public health, public opinion, and values and beliefs. Beyond evaluating average alignment, we also report how alignment varies across specific groups. Our broad findings provide insights into the distributional alignment of LLM generations with diverse populations. By conducting evaluation over many LLMs and prompting strategies, we provide a benchmark to stimulate future research.
%U https://aclanthology.org/2026.acl-long.1583/
%P 34277-34305
Markdown (Informal)
[Improving the Distributional Alignment of LLMs using Supervision](https://aclanthology.org/2026.acl-long.1583/) (Kambhatla et al., ACL 2026)
ACL
- Gauri Kambhatla, Sanjana Gautam, Angela Zhang, Alexander Liu, Ravi Srinivasan, Junyi Jessy Li, and Matthew Lease. 2026. Improving the Distributional Alignment of LLMs using Supervision. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 34277–34305, San Diego, California, United States. Association for Computational Linguistics.