在巴基斯坦多种语言中框架化政治偏见
Framing Political Bias in Multilingual LLMs Across Pakistani Languages
AI总结：
研究评估了巴基斯坦五种语言中13种先进LLM的政治偏见，发现其在区域语言中表现出更集权的框架，强调文化适应的偏见审计框架的重要性。
摘要：
大型语言模型（LLMs）日益塑造公共讨论，但大多数对政治和经济偏见的评估主要集中在高资源、西方语言和语境上。这在低资源、多语言地区如巴基斯坦留下关键盲区，其中语言身份与政治、宗教和区域意识形态紧密相关。我们对13种最先进的LLMs在五种巴基斯坦语言（乌尔都语、旁遮普语、 Sindhi、普什图语和俾路支语）中政治偏见进行了系统评估。我们的框架整合了文化适应的政见测验（PCT）与多层级框架分析，捕捉意识形态立场（经济/社会轴）和风格框架（内容、语气、强调）。提示与11个特定于巴基斯坦的社政治主题对齐。结果表明，尽管LLMs主要反映与西方训练数据一致的自由左派取向，但在区域语言中表现出更集权的框架，突显了语言条件下的意识形态调节。我们还发现跨语言一致的模型特定偏见模式。这些发现表明，在全球NLP中需要基于文化的、多语言偏见审计框架。
英文摘要：
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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