Large language models (LLMs) have rapidly become indispensable tools for acquiring information and supporting human decision-making. However, ensuring that these models uphold fairness across varied contexts is critical to their safe and responsible deployment. In this study, we undertake a comprehensive examination of four widely adopted LLMs, probing their underlying biases and inclinations across the dimensions of politics, ideology, alliance, language, and gender. Through a series of carefully designed experiments, we investigate their political neutrality using news summarization, ideological biases through news stance classification, tendencies toward specific geopolitical alliances via United Nations voting patterns, language bias in the context of multilingual story completion, and gender-related affinities as revealed by responses to the World Values Survey. Results indicate that while the LLMs are aligned to be neutral and impartial, they still show biases and affinities of different types.

翻译：大规模语言模型（LLMs）已迅速成为获取信息和支持人类决策不可或缺的工具。然而，确保这些模型在不同情境下维护公平性，对于其安全且负责任的部署至关重要。本研究对四种广泛采用的LLMs进行了全面审查，从政治、意识形态、联盟、语言和性别维度探其固有偏见与倾向。通过一系列精心设计的实验，我们利用新闻摘要分析其政治中立性，通过新闻立场分类探究意识形态偏见，依据联合国投票模式考察其特定地缘政治联盟倾向，在多语言故事完成情境中检验语言偏见，并借助世界价值观调查的反应揭示与性别相关的倾向。结果表明，尽管LLMs被校准为保持中立与公正，但它们仍表现出不同类型和程度的偏见与倾向。