用维多利亚风格提示词生成小说，发现模型能稳定输出时代特征文本。
When Victorian Becomes a Prompt: Literary Periodization as a Generative Constraint in 100 AI-Generated Novels
- 用文学时期标签作提示词，引导AI生成具有时代特征的文本。
- 维多利亚提示使GPT和Qwen生成内容在语法特征上显著偏向19世纪。
- 该方法对研究文学风格迁移和模型语言表征有参考价值。
生成式AI颠覆了传统文学史的分期方式：过去由文本决定时期标签，现在标签可先于内容出现并影响生成结果。本文定义并测试了‘生成式分期’概念，通过GPT、Qwen和Llama在维多利亚风格与零风格条件下生成100部完整小说进行验证。使用基于19世纪文学训练的周期对齐评分（PAS），以话题降维后的语法特征为指标，评估生成文本与人类零风格文本的基准对齐度。结果显示，维多利亚提示显著推动GPT和Qwen在语法结构上向19世纪靠拢，而Llama表现不显著；对模型进行仅维多利亚风格重校准及使用更难对比语料库后，仍保持该效应。跨模型迁移分析表明，存在共享的语法演变方向。其测量目标是更广泛的19世纪整体风格，而非维多利亚时期本身。
原文摘要 · Abstract (English)
Generative AI inverts the typical periodization of literary history: the periodizing tag Victorian can now come first and influence what is written. Generative periodization, defined and tested here, describes the use of literary-period designations in generating texts. I test this approach on 100 book-length novels produced under Victorian and Zero-Style conditions using GPT, Qwen, and Llama workflows. The Period Alignment Score (PAS), trained on nineteenth-century literature and benchmarked against human Zero-Style prose, assesses alignment using topic-reduced grammatical features. Victorian prompts produce consistent historical-direction shifts in GPT and Qwen, but not robustly in Llama. Victorian-only recalibration and harder comparison corpora preserve the GPT and Qwen effects. Cross-model transfer also shows a shared direction of grammatical change. The measurable target is the broader nineteenth century rather than the Victorian period per se.
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