通过社会世界观的视角分析大型语言模型的认知差异
Analyzing Cognitive Differences Among Large Language Models through the Lens of Social Worldview
AI总结：
本研究通过社会世界观分类法分析大型语言模型的认知差异，揭示其在动态社会环境中的适应性与认知灵活性。
摘要：
大型语言模型显著影响社会互动、决策和信息传播，凸显了理解这些系统中隐含的隐性社会认知态度，即
英文摘要：
Large Language Models significantly influence social interactions, decision-making, and information dissemination, underscoring the need to understand the implicit socio-cognitive attitudes, referred to as "worldviews", encoded within these systems. Unlike previous studies predominantly addressing demographic and ethical biases as fixed attributes, our study explores deeper cognitive orientations toward authority, equality, autonomy, and fate, emphasizing their adaptability in dynamic social contexts. We introduce the Social Worldview Taxonomy (SWT), an evaluation framework grounded in Cultural Theory, operationalizing four canonical worldviews, namely Hierarchy, Egalitarianism, Individualism, and Fatalism, into quantifiable sub-dimensions. Through extensive analysis of 28 diverse LLMs, we identify distinct cognitive profiles reflecting intrinsic model-specific socio-cognitive structures. Leveraging principles from Social Referencing Theory, our experiments demonstrate that explicit social cues systematically modulate these profiles, revealing robust patterns of cognitive adaptability. Our findings provide insights into the latent cognitive flexibility of LLMs and offer computational scientists practical pathways toward developing more transparent, interpretable, and socially responsible AI systems
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
