← 研踪 PaperTrailACL Anthology · ACL 2026:acl:2026.acl-long.1689 · 记录 #1
Framing Political Bias in Multilingual LLMs Across Pakistani Languages
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 21:50
摘要
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.
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记录 #1 · 2026-07 · 最新观测版本
Framing Political Bias in Multilingual LLMs Across Pakistani Languages
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.
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