This chapter argues for more informed target metrics for the statistical processing of stylistic variation in text collections. Much as operationalised relevance proved a useful goal to strive for in information retrieval, research in textual stylistics, whether application oriented or philologically inclined, needs goals formulated in terms of pertinence, relevance, and utility - notions that agree with reader experience of text. Differences readers are aware of are mostly based on utility - not on textual characteristics per se. Mostly, readers report stylistic differences in terms of genres. Genres, while vague and undefined, are well-established and talked about: very early on, readers learn to distinguish genres. This chapter discusses variation given by genre, and contrasts it to variation occasioned by individual choice.
Interpretable Stylistic Variation in Human and LLM Writing Across Genres, Models, and Decoding Strategies
Software Engineering Institute, AI Division, Carnegie Mellon University（卡内基梅隆大学软件工程院，人工智能分部）；Department of Statistics & Data Science, Carnegie Mellon University（卡内基梅隆大学统计与数据科学系）
Lightweight Stylistic Consistency Profiling: Robust Detection of LLM-Generated Textual Content for Multimedia Moderation
School of Computer Science, Shanghai Jiao Tong University（上海交通大学计算机科学学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）
An Information-Theoretic Approach to Identifying Formulaic Clusters in Textual Data
Department of Statistics and Data Science, The Hebrew University of Jerusalem（统计与数据科学系，耶路撒冷希伯来大学）；Department of Statistics（统计系）；Data Science, The Hebrew University of Jerusalem（数据科学，耶路撒冷希伯来大学）；Faculty of Theology, Carl von Ossietzky Universität Oldenburg（神学系，卡尔·冯·奥西特齐克大学）
Labelled network subgraphs reveal stylistic subtleties in written texts
Institute of Mathematics and Computer Science, University of São Paulo（圣保罗大学数学与计算机科学研究所）；Department of Computer Science, University of Toronto（多伦多大学计算机科学系）
An Overview on Controllable Text Generation via Variational Auto-Encoders
University of Chinese Academy of Sciences（中国科学院大学）；Noah’s Ark Lab, Huawei（华为诺亚方舟实验室）；Huawei Technologies Ltd.（华为技术有限公司）
基于神经元的生成建模的最新进展重新点燃了人们对拥有能够与人类对话并能够理解自然语言的计算机系统的希望。深度神经架构的使用已经在许多上下文和任务中进行了大量探索，以满足各种用户需求。一方面，生成满足特定要求的文本内容是模型与不同人群无缝对话的优先事项。另一方面，潜变量模型（LVM），如变分自动编码器（VAE）作为最流行的生成模型的流派之一，旨在表征文本数据的分布模式。因此，他们天生就有能力学习值得探索的可控追求的整体文本特征。\noindent这篇综述介绍了现有的生成方案，与文本变分自动编码器相关的问题，并回顾了几个关于可控生成的应用程序，这些应用程序是这些通用配方的实例，\footnote{A detailed paper list is available at \url{https://github.com/ImKeTT/CTG-latentAEs}}以及相关的数据集，度量和讨论未来的研究。希望这篇综述能对变分自动编码器下可控语言生成的问题、流行方法和原始思想提供一个概述。
How Robust Is Multimodal Claim Verification to LLM Rewriting?
NII LLMC, Japan（国立情报学研究所 LLMC，日本）；National Institute of Informatics, Japan（国立情报学研究所，日本）；University of Göttingen, Germany（哥廷根大学，德国）；University of Bologna, Italy（博洛尼亚大学，意大利）；The University of Tokyo, Japan（东京大学，日本）；Inria, LS2N, Nantes Université, France（法国国家信息与自动化研究所，LS2N，南特大学，法国）
