当写作风格发生漂移：在体裁、时间与AI时代的分布偏移下对作者验证进行基准测试

中文摘要 AI 辅助

作者验证（Authorship Verification, AV）假设作者的写作风格足够稳定，可用于将其与其他作者的风格区分开。然而在实际应用中，这一假设会受到体裁、时间以及AI辅助写作引发的分布偏移的挑战。现有AV基准通常单独研究这些因素，且主要聚焦于英语，这限制了我们对模型在现实条件下鲁棒性的理解。我们推出AVShift，这是首个用于在多种分布偏移下系统评估AV的德语基准。AVShift包含超过15万对文本，涵盖三种体裁和21年的时间跨度，可在统一框架内对跨体裁、时间以及AI时代的偏移进行受控评估。我们对代表性的基于特征、基于嵌入以及基于大语言模型（LLM）的方法进行了基准测试。实验表明，微调后的LLM在跨体裁场景下的泛化能力最佳，且从风格多样的训练数据中获益显著。我们进一步证明，时间漂移是影响AV的最强因素之一，随着文档间时间间隔的增大，性能会显著下降。相比之下，我们在AVShift中未发现可测量的AI时代分布偏移的证据。最后，我们的特征分析揭示了在不同体裁间保持稳定的风格特征，而这些特征的相对重要性会因具体的体裁转换而异。我们发布了AVShift及相关代码，以供未来研究使用。

英文摘要

Authorship verification (AV) assumes that an author's writing style remains sufficiently stable to distinguish it from that of other writers. In practice, however, this assumption is challenged by distribution shifts caused by changes in genre, time, and AI-assisted writing. Existing AV benchmarks typically study these factors in isolation and focus predominantly on English, limiting our understanding of model robustness under realistic conditions. We introduce AVShift, the first German benchmark for systematically evaluating AV under multiple distribution shifts. AVShift comprises over 150,000 text pairs spanning three genres and 21 years, enabling controlled evaluation of cross-genre, temporal, and AI-era shifts within a unified framework. We benchmark representative feature-based, embedding-based, and LLM-based approaches. Our experiments show that fine-tuned LLMs generalize best across genres and benefit substantially from stylistically diverse training data. We further demonstrate that temporal drift is one of the strongest factors affecting AV, with performance degrading significantly as the time gap between documents increases. In contrast, we find no evidence of a measurable AI-era distribution shift within AVShift. Finally, our feature analysis reveals stylistic features that remain stable across genres, while their relative importance varies depending on the specific genre transition. We release AVShift and our code for future research.

发表机构

• University of Technology Nuremberg (UTN)（纽伦堡工业大学（UTN））

机构由 AI 辅助整理，请以论文原文为准。