LITERARYBIGFIVE: Author-Personalized Text Generation in a Unified Interpretable Space

2026-08-24

AI Summary

该研究提出LiteraryBigFive框架，通过将作者写作风格特征映射到统一且可解释的空间内，解决个性化文本生成问题，并引入了可解释的引导机制以实现目标风格的文本生成。

Abstract

Personalized text generation for authors and literary writing is essential for applications such as adaptive writing assistants, creative support tools, and computational literary analysis. However, existing approaches to author modeling and personalization often represent writing behavior as independent labels, requiring large-scale corpus collection or fine-tuning for each author or stylistic category. Such formulations are costly, difficult to interpret, and poorly suited for generalizing across authors. Inspired by the Big Five model's dimensional view of personality, we propose LiteraryBigFive, a framework that reframes authorial writing characteristics as coordinates within a unified and interpretable space. In this space, we derive each interpretable axis (e.g., Classicism, Emotionality) from activation-space contrasts between author-written and neutral passages, yielding distinct stylistic dimensions that allow texts or authors to be positioned within a five-dimensional system. Beyond localizing different authors, we further introduce an interpretable steering mechanism, which adaptively guides text generation toward target coordinates to perform author-personalized writing. Experimental results show that LiteraryBigFive improves authorial expressiveness while preserving semantic fidelity. The derived author per-axis scores strongly correlate with real-world literary consensus, offering transparent and interpretable explanations of author-specific generation behavior: https://github.com/Znull-1220/LiteraryBigFive.

Problem

• author modeling
• personalization
• writing behavior
• large-scale corpus
• fine-tuning

Innovation

• LiteraryBigFive
• interpretable space
• author-personalized text generation
• stylistic dimensions
• steering mechanism