Generation, Evaluation, and Explanation of Novelists' Styles with Single-Token Prompts

• URL: http://arxiv.org/abs/2511.20459v1
• Date: Tue, 25 Nov 2025 16:25:44 GMT
• Title: Generation, Evaluation, and Explanation of Novelists' Styles with Single-Token Prompts
• Authors: Mosab Rezaei, Mina Rajaei Moghadam, Abdul Rahman Shaikh, Hamed Alhoori, Reva Freedman,
• Abstract summary: We present a framework for both generating and evaluating sentences in the style of 19th-century novelists.<n>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.
• Score: 3.7189423451031356
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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