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Probing Politico-Economic Bias in Multilingual Large Language Models: A Cultural Analysis of Low-Resource Pakistani Languages

29 May 2025
Afrozah Nadeem
Mark Dras
Usman Naseem
ArXiv (abs)PDFHTML
Main:8 Pages
19 Figures
Bibliography:3 Pages
2 Tables
Appendix:33 Pages
Abstract

Large Language Models (LLMs) are increasingly shaping public discourse, yet their politico-economic biases remain underexamined in non-Western and low-resource multilingual contexts. This paper presents a systematic analysis of political bias in 13 state-of-the-art LLMs across five low-resource languages spoken in Pakistan: Urdu, Punjabi, Sindhi, Balochi, and Pashto. We propose a novel framework that integrates an adapted Political Compass Test (PCT) with a multi-level framing analysis. Our method combines quantitative assessment of political orientation across economic (left-right) and social (libertarian-authoritarian) axes with qualitative analysis of framing through content, style, and emphasis. We further contextualize this analysis by aligning prompts with 11 key socio-political themes relevant to Pakistani society. Our results reveal that LLMs predominantly align with liberal-left values, echoing Western training data influences, but exhibit notable shifts toward authoritarian framing in regional languages, suggesting strong cultural modulation effects. We also identify consistent model-specific bias signatures and language-conditioned variations in ideological expression. These findings show the urgent need for culturally grounded, multilingual bias auditing frameworks.

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@article{nadeem2025_2506.00068,
  title={ Probing Politico-Economic Bias in Multilingual Large Language Models: A Cultural Analysis of Low-Resource Pakistani Languages },
  author={ Afrozah Nadeem and Mark Dras and Usman Naseem },
  journal={arXiv preprint arXiv:2506.00068},
  year={ 2025 }
}
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