超越工具性和替代性范式：引入机器文化作为大型语言模型中的涌现现象
Beyond Instrumental and Substitutive Paradigms: Introducing Machine Culture as an Emergent Phenomenon in Large Language Models
AI总结：
本研究提出机器文化作为大型语言模型中的一种新兴现象，挑战传统工具性和替代性范式，揭示模型在文化表现上的独特特性。
摘要：
近期的研究通常通过 either an Instrumental Paradigm（将模型视为开发者文化的反映）或 a Substitutive Paradigm（将模型视为双语代理，根据语言切换文化框架）来表征大型语言模型（LLMs）。本研究通过提出 Machine Culture 作为一种新兴、独特的现象来挑战这些拟人化框架。我们采用 2（模型来源：美国 vs 中国）×2（提示语言：英语 vs 中文）的因子设计，跨八个多模态任务进行研究，独特地纳入图像生成和解释以扩展分析范围。结果揭示了与主流范式不一致的现象：模型来源并未预测文化一致性，美国模型经常表现出通常与东亚数据相关的“整体性”特征。同样，提示语言并未触发稳定的文化框架切换；相反，我们观察到文化反转，英语提示意外地引发了更高的上下文注意力。关键的是，我们识别出一种新现象，称为 Service Persona Camouflage：通过人类反馈强化学习（RLHF）在情感任务中将文化差异压缩成一个超积极、零方差的“帮助助手”人设。我们得出结论：LLMs 并不模拟人类文化，而是表现出一种新兴的机器文化——一种由高维空间中的叠加和安全对齐中的模式崩溃塑造的概率现象。
英文摘要：
Recent scholarship typically characterizes Large Language Models (LLMs) through either an \textit{Instrumental Paradigm} (viewing models as reflections of their developers' culture) or a \textit{Substitutive Paradigm} (viewing models as bilingual proxies that switch cultural frames based on language). This study challenges these anthropomorphic frameworks by proposing \textbf{Machine Culture} as an emergent, distinct phenomenon. We employed a 2 (Model Origin: US vs. China) $\times$ 2 (Prompt Language: English vs. Chinese) factorial design across eight multimodal tasks, uniquely incorporating image generation and interpretation to extend analysis beyond textual boundaries. Results revealed inconsistencies with both dominant paradigms: Model origin did not predict cultural alignment, with US models frequently exhibiting ``holistic'' traits typically associated with East Asian data. Similarly, prompt language did not trigger stable cultural frame-switching; instead, we observed \textbf{Cultural Reversal}, where English prompts paradoxically elicited higher contextual attention than Chinese prompts. Crucially, we identified a novel phenomenon termed \textbf{Service Persona Camouflage}: Reinforcement Learning from Human Feedback (RLHF) collapsed cultural variance in affective tasks into a hyper-positive, zero-variance ``helpful assistant'' persona. We conclude that LLMs do not simulate human culture but exhibit an emergent Machine Culture -- a probabilistic phenomenon shaped by \textit{superposition} in high-dimensional space and \textit{mode collapse} from safety alignment.
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。
