@inproceedings{kim-etal-2026-polar,
title = "Polar: A Benchmark for Evaluating Political Bias in {LLM}s",
author = "Kim, Sangho and
Kim, Heejin and
Jeon, Hyunggeun and
Park, Yoonhee and
Lee, Jaejin",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {IJCNLP}-{AACL} 2026",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-aacl.181/",
pages = "3575--3635",
ISBN = "979-8-89176-444-6",
abstract = "Political bias in large language models (LLMs) is increasingly significant, but difficult to measure reproducibly across political and linguistic contexts. We introduce Polar, a 4,026-instance multiple-choice benchmark that measures political bias through option-level likelihoods rather than prompt-based generation. Polar covers two ideological axes and eight issue categories derived from the Manifesto Project, and evaluates models in parallel across U.S. and South Korean political contexts. Across 38 LLMs, measured bias varies systematically with political context, issue category, model group, and presentation language. All models lean left-progressive on U.S. political content, but show more centered and mixed patterns on South Korean content. Translation experiments further show that presentation language alone can shift measured bias. These findings highlight the need for multilingual and cross-contextual evaluation of political bias in LLMs."
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<abstract>Political bias in large language models (LLMs) is increasingly significant, but difficult to measure reproducibly across political and linguistic contexts. We introduce Polar, a 4,026-instance multiple-choice benchmark that measures political bias through option-level likelihoods rather than prompt-based generation. Polar covers two ideological axes and eight issue categories derived from the Manifesto Project, and evaluates models in parallel across U.S. and South Korean political contexts. Across 38 LLMs, measured bias varies systematically with political context, issue category, model group, and presentation language. All models lean left-progressive on U.S. political content, but show more centered and mixed patterns on South Korean content. Translation experiments further show that presentation language alone can shift measured bias. These findings highlight the need for multilingual and cross-contextual evaluation of political bias in LLMs.</abstract>
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%0 Conference Proceedings
%T Polar: A Benchmark for Evaluating Political Bias in LLMs
%A Kim, Sangho
%A Kim, Heejin
%A Jeon, Hyunggeun
%A Park, Yoonhee
%A Lee, Jaejin
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-444-6
%F kim-etal-2026-polar
%X Political bias in large language models (LLMs) is increasingly significant, but difficult to measure reproducibly across political and linguistic contexts. We introduce Polar, a 4,026-instance multiple-choice benchmark that measures political bias through option-level likelihoods rather than prompt-based generation. Polar covers two ideological axes and eight issue categories derived from the Manifesto Project, and evaluates models in parallel across U.S. and South Korean political contexts. Across 38 LLMs, measured bias varies systematically with political context, issue category, model group, and presentation language. All models lean left-progressive on U.S. political content, but show more centered and mixed patterns on South Korean content. Translation experiments further show that presentation language alone can shift measured bias. These findings highlight the need for multilingual and cross-contextual evaluation of political bias in LLMs.
%U https://aclanthology.org/2026.findings-aacl.181/
%P 3575-3635
Markdown (Informal)
[Polar: A Benchmark for Evaluating Political Bias in LLMs](https://aclanthology.org/2026.findings-aacl.181/) (Kim et al., Findings 2026)
ACL
- Sangho Kim, Heejin Kim, Hyunggeun Jeon, Yoonhee Park, and Jaejin Lee. 2026. Polar: A Benchmark for Evaluating Political Bias in LLMs. In Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026, pages 3575–3635, Hengqin, China. Association for Computational Linguistics.