用AI生成55万虚拟受访者，发现其文化品味高度失真。
Tastes without distinction: silicon samples and the synthetic construction of tastes
- 用三大厂商LLM生成554,940个虚拟调查对象。
- 虚拟样本普遍偏好过度，真实味觉关系被彻底扭曲。
- 适合关注AI对文化研究干扰的社科与数据科学家。
大型语言模型虽能模仿公众态度，但其在文化品味上的表现仍存疑问。随着市场研究公司开始使用‘合成’调查样本，且传统问卷数据正被大量AI生成内容污染，该问题日益紧迫。本研究基于硅样本（silicon sampling）方法，扩展了生态、关系与位置保真度的考量维度，利用OpenAI、Anthropic和DeepSeek的LLM生成了554,940个来自美国艺术参与调查（SPPA）的虚拟受访者。结果表明，这些硅样本的文化品味是高度风格化的虚假复制品：个体层面表现出系统性偏好评好，此偏差无法由现有文献中的WEIRD偏见解释；真实品味间的复杂关系结构在硅样本中完全扭曲；社会空间与品味之间的已知关联几乎未被保留——年龄-品味关系被幼稚化，阶级-品味关联被复古化，性别与种族-品味关联被夸张化。
原文摘要 · Abstract (English)
Large-language models have proven to be remarkable if inconsistent parrots of public attitudes and opinions. The extent to which LLMs are able to produce reasonable approximations of cultural taste remains an open empirical question that becomes more urgent by the day, with market research companies already offering provisional 'synthetic' survey panels and the contamination of standard survey data from LLM-generated responses. In this study, we build on past work on silicon sampling by extending considerations of their ecological, relational, and positional fidelity in the doomain of cultural tastes. We use large-language models from OpenAI, Anthropic, and DeepSeek to produce 554,940 silicon surrogates of survey respondents from the Survey of Public Participation in the Arts (SPPA). We find these silicon surrogates' tastes to be highly stylized facsimiles of human tastes. First, silicon samples are super-omnivorous with a systematic postive-bias for liking. These individual-level bias of silicon samples are not well-explained by the WEIRD-bias often discussed in the literature. Second, the complex relationality in real taste structures is completely distorted among silicon samples. Third, very little of the known cultural alignment between tastes and social space are preserved. Silicon samples juvenilize age-taste associations, resurrect anachronistic class-taste associations, and caricaturize gender- and race-taste associations. Key words: AI, taste, consumption, culture, silicon sampling, meta-analysis.
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