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    <abstract>Large Language Models (LLMs) increasingly shape public discourse, yet most evaluations of economic and political bias have focused on high-resource Western languages and contexts. This leaves a blind spots in low-resource, multilingual regions such as Pakistan, where linguistic identity is closely tied to regional, religious, and political ideologies. We present a systematic evaluation of political bias in 13 state-of-the-art LLMs across five Pakistani languages: Urdu, Punjabi, Sindhi, Pashto, and Balochi. Our framework integrates a culturally adapted Political Compass Test (PCT) with multi-level framing analysis, capturing both ideological stance (economic/social axes) and stylistic framing (content, tone, emphasis). The prompts are aligned with 11 socio-political themes specific to the Pakistani context. The results show that while LLMs significantly reflect liberal-left orientations consistent with Western training data, they exhibit more authoritarian framing in regional languages, highlighting language-conditioned ideological modulation. We also identify model-specific bias patterns in all languages. These findings show the need for culturally grounded multilingual bias examining frameworks in NLP. Code and dataset are available.</abstract>
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%0 Conference Proceedings
%T Framing Political Bias in Multilingual LLMs Across Pakistani Languages
%A Nadeem, Afrozah
%A Dras, Mark
%A Naseem, Usman
%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 nadeem-etal-2026-framing
%X Large Language Models (LLMs) increasingly shape public discourse, yet most evaluations of economic and political bias have focused on high-resource Western languages and contexts. This leaves a blind spots in low-resource, multilingual regions such as Pakistan, where linguistic identity is closely tied to regional, religious, and political ideologies. We present a systematic evaluation of political bias in 13 state-of-the-art LLMs across five Pakistani languages: Urdu, Punjabi, Sindhi, Pashto, and Balochi. Our framework integrates a culturally adapted Political Compass Test (PCT) with multi-level framing analysis, capturing both ideological stance (economic/social axes) and stylistic framing (content, tone, emphasis). The prompts are aligned with 11 socio-political themes specific to the Pakistani context. The results show that while LLMs significantly reflect liberal-left orientations consistent with Western training data, they exhibit more authoritarian framing in regional languages, highlighting language-conditioned ideological modulation. We also identify model-specific bias patterns in all languages. These findings show the need for culturally grounded multilingual bias examining frameworks in NLP. Code and dataset are available.
%R 10.18653/v1/2026.acl-long.1689
%U https://aclanthology.org/2026.acl-long.1689/
%U https://doi.org/10.18653/v1/2026.acl-long.1689
%P 36444-36486

Markdown (Informal)

[Framing Political Bias in Multilingual LLMs Across Pakistani Languages](https://aclanthology.org/2026.acl-long.1689/) (Nadeem et al., ACL 2026)

• Framing Political Bias in Multilingual LLMs Across Pakistani Languages (Nadeem et al., ACL 2026)

ACL

• Afrozah Nadeem, Mark Dras, and Usman Naseem. 2026. Framing Political Bias in Multilingual LLMs Across Pakistani Languages. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 36444–36486, San Diego, California, United States. Association for Computational Linguistics.