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

• URL: http://arxiv.org/abs/2506.00068v1
• Date: Thu, 29 May 2025 15:15:42 GMT
• Title: Probing Politico-Economic Bias in Multilingual Large Language Models: A Cultural Analysis of Low-Resource Pakistani Languages
• Authors: Afrozah Nadeem, Mark Dras, Usman Naseem,
• Abstract summary: This paper presents a systematic analysis of political bias in 13 large language models (LLMs) across five low-resource languages spoken in Pakistan.<n>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.<n>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.
• Score: 6.5137518437747
• License: http://creativecommons.org/licenses/by/4.0/
• 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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