Abstract
Computational stylometry analyzes writing style through quantitative patterns in text, supporting applications from forensic tasks such as identity linking and plagiarism detection to literary attribution in the humanities. Supervised and contrastive approaches rely on data with spurious correlations and often confuse style with topic. Despite their natural use in AI-generated text detection, the CLM pre-training of modern LLMs has been scarcely leveraged for general authorship problems. We propose a novel unsupervised approach based on this extensive pre-training and the in-context learning capabilities of LLMs, employing the log-probabilities of an LLM to measure style transferability from one text to another. Our method significantly outperforms LLM prompting approaches of comparable scale and achieves higher accuracy than contrastively trained baselines when controlling for topical correlations. Moreover, performance scales fairly consistently with the size of the base model and, in the case of authorship verification, with an additional mechanism that increases test-time computation; enabling flexible trade-offs between computational cost and accuracy.
Abstract (translated)
计算性文体测量通过文本中的定量模式分析写作风格，支持从法医任务（如身份链接和抄袭检测）到人文学科中文学归属等各类应用。虽然监督学习和对比方法依赖于带有伪相关的数据，并且常常将写作风格与主题混淆，但现代大型语言模型（LLM）的CLM预训练在一般作者识别问题上却鲜少被利用。我们提出了一种基于广泛预训练及LLM上下文学习能力的新颖无监督方法，通过使用LLM的日志概率来测量一种文本向另一种文本的写作风格可转移性。我们的方法显著优于规模相当的提示式LLM方法，并且在控制主题相关性的前提下，比对比训练基线获得了更高的准确率。此外，性能随着基础模型大小的增长而相对一致地提升；而在作者验证的情况下，通过增加测试时计算量的额外机制，使计算成本与准确性之间的灵活权衡成为可能。
URL
https://arxiv.org/abs/2510.13302
