Framing Political Bias in Multilingual LLMs Across Pakistani Languages

• URL: http://arxiv.org/abs/2506.00068v2
• Date: Thu, 31 Jul 2025 04:41:18 GMT
• Title: Framing Political Bias in Multilingual LLMs Across Pakistani Languages
• Authors: Afrozah Nadeem, Mark Dras, Usman Naseem,
• Abstract summary: We present a systematic evaluation of political bias in 13 state-of-the-art Large Language Models (LLMs) across five Pakistani languages.<n>Our framework integrates a culturally adapted Political Compass Test (PCT) with multi-level framing analysis.<n>Results show that while LLMs predominantly reflect liberal-left orientations consistent with Western training data, they exhibit more authoritarian framing in regional languages.
• Score: 6.5137518437747
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) increasingly shape public discourse, yet most evaluations of political and economic bias have focused on high-resource, Western languages and contexts. This leaves critical blind spots in low-resource, multilingual regions such as Pakistan, where linguistic identity is closely tied to political, religious, and regional 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). Prompts are aligned with 11 socio-political themes specific to the Pakistani context. Results show that while LLMs predominantly 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 consistent model-specific bias patterns across languages. These findings show the need for culturally grounded, multilingual bias auditing frameworks in global NLP.

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