用单标记提示让大模型模仿19世纪小说家文风，还能自动评估和解释风格特征。
Generation, Evaluation, and Explanation of Novelists' Styles with Single-Token Prompts
- 仅用单个词提示微调大模型，生成仿写小说家风格的文本。
- AI检测器识别出作者独特语言模式，准确率优于人工判断。
- 结合注意力分析与梯度方法，揭示风格模仿的关键语言线索。
大语言模型的发展为风格分析（stylometry）带来了新机遇，但两大挑战仍存：缺乏配对数据时如何训练生成模型，以及如何在不依赖人类判断的情况下评估文本风格。本文提出一个框架，可生成并评估19世纪小说家（如狄更斯、奥斯汀、马克·吐温、艾尔科特、梅尔维尔）的文风。通过极简的单标记提示微调大模型，生成其风格文本；采用基于Transformer的检测器，以真实语句为训练数据，既作分类器又用于风格解释。辅以句法对比和可解释AI方法（注意力与梯度分析），识别驱动风格模仿的语言特征。结果表明，生成文本忠实反映作者独特模式，且AI评估可靠替代人工判断。所有成果已公开发布。
原文摘要 · Abstract (English)
Recent advances in large language models have created new opportunities for stylometry, the study of writing styles and authorship. Two challenges, however, remain central: training generative models when no paired data exist, and evaluating stylistic text without relying only on human judgment. In this work, we present a framework for both generating and evaluating sentences in the style of 19th-century novelists. Large language models are fine-tuned with minimal, single-token prompts to produce text in the voices of authors such as Dickens, Austen, Twain, Alcott, and Melville. To assess these generative models, we employ a transformer-based detector trained on authentic sentences, using it both as a classifier and as a tool for stylistic explanation. We complement this with syntactic comparisons and explainable AI methods, including attention-based and gradient-based analyses, to identify the linguistic cues that drive stylistic imitation. Our findings show that the generated text reflects the authors' distinctive patterns and that AI-based evaluation offers a reliable alternative to human assessment. All artifacts of this work are published online.
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