Authorship style transfer aims to rewrite a given text into a specified target while preserving the original meaning in the source. Existing approaches rely on the availability of a large number of target style exemplars for model training. However, these overlook cases where a limited number of target style examples are available. The development of parameter-efficient transfer learning techniques and policy optimization (PO) approaches suggest lightweight PO is a feasible approach to low-resource style transfer. In this work, we propose a simple two-stage tune-and-optimize technique for low-resource textual style transfer. We apply our technique to authorship transfer as well as a larger-data native language style task and in both cases find it outperforms state-of-the-art baseline models.
With such increasing popularity and availability of digital text data, authorships of digital texts can not be taken for granted due to the ease of copying and parsing. This paper presents a new text style analysis called natural frequency zoned word distribution analysis (NFZ-WDA), and then a basic authorship attribution scheme and an open authorship attribution scheme for digital texts based on the analysis. NFZ-WDA is based on the observation that all authors leave distinct intrinsic word usage traces on texts written by them and these intrinsic styles can be identified and employed to anal…
Stylometry Analysis of Multi-authored Documents for Authorship and Author Style Change Detection
Abasyn University Islamabad Campus（阿巴辛大学伊斯兰堡校区）；University of Engineering and Technology, Peshawar（白沙瓦工程技术大学）；National University of Science and Technology（国立科技大学）；University of Western Australia（西澳大学）；Munster Technological University（芒斯特理工大学）
Technical University of Munich（慕尼黑工业大学）；Humboldt-Universität zu Berlin（柏林洪堡大学）；University of Oxford（牛津大学）；Martin-Luther-Universität Halle-Wittenberg（马丁·路德·哈勒-维滕贝格大学）；University of the Aegean（爱琴海大学）；Bauhaus-Universität Weimar（魏玛包豪斯大学）；Leipzig University（莱比锡大学）
The prerequisite of many approaches to authorship analysis is a representation of writing style. But despite decades of research, it still remains unclear to what extent commonly used and widely accepted representations like character trigram frequencies actually represent an author's writing style, in contrast to more domain-specific style components or even topic. We address this shortcoming for the first time in a novel experimental setup of fixed authors but swapped domains between training and testing. With this setup, we reveal that approaches using character trigram features are highly …
Minds, Machines, and Society Group（心智、机器与社会研究组）；Department of Computer Science, Dartmouth College（达特茅斯学院计算机科学系）；Dartmouth College（达特茅斯学院）
Authorship identification tasks, which rely heavily on linguistic styles, have always been an important part of Natural Language Understanding (NLU) research. While other tasks based on linguistic style understanding benefit from deep learning methods, these methods have not behaved as well as traditional machine learning methods in many authorship-based tasks. With these tasks becoming more and more challenging, however, traditional machine learning methods based on handcrafted feature sets are already approaching their performance limits. Thus, in order to inspire future applications of deep…
We describe a technique for attributing parts of a written text to a set of unknown authors. Nothing is assumed to be known a priori about the writing styles of potential authors. We use multiple independent clusterings of an input text to identify parts that are similar and dissimilar to one another. We describe algorithms necessary to combine the multiple clusterings into a meaningful output. We show results of the application of the technique on texts having multiple writing styles.
Writing style is a combination of consistent decisions associated with a specific author at different levels of language production, including lexical, syntactic, and structural. In this paper, we introduce a style-aware neural model to encode document information from three stylistic levels and evaluate it in the domain of authorship attribution. First, we propose a simple way to jointly encode syntactic and lexical representations of sentences. Subsequently, we employ an attention-based hierarchical neural network to encode the syntactic and semantic structure of sentences in documents while…
University of Electronic Science and Technology of China（电子科技大学）；Singapore Management University（新加坡管理大学）；University of Saskatchewan（萨斯喀彻温大学）
Authorship attribution (AA), which is the task of finding the owner of a given text, is an important and widely studied research topic with many applications. Recent works have shown that deep learning methods could achieve significant accuracy improvement for the AA task. Nevertheless, most of these proposed methods represent user posts using a single type of feature (e.g., word bi-grams) and adopt a text classification approach to address the task. Furthermore, these methods offer very limited explainability of the AA results. In this paper, we address these limitations by proposing DeepStyl…
We present our approach for computer-aided social media text authorship attribution based on recent advances in short text authorship verification. We use various natural language techniques to create word-level and character-level models that act as hidden layers to simulate a simple neural network. The choice of word-level and character-level models in each layer was informed through validation performance. The output layer of our system uses an unweighted majority vote vector to arrive at a conclusion. We also considered writing bias in social media posts while collecting our training datas…
ChatGPT-generated texts show authorship traits that identify them as non-human
Università degli Studi di Pavia（帕维亚大学）；University of Birmingham（伯明翰大学）；Universitat Autònoma de Barcelona（巴塞罗那自治大学）；Institució Catalana de Recerca i Estudis Avançats (ICREA)（加泰罗尼亚高等研究院）
Abasyn University Islamabad Campus（阿巴辛大学伊斯兰堡校区）；Jeddah International College（吉达国际学院）；AJOU University（亚洲大学）；Munster Technological University（芒斯特理工大学）
Style analysis, which is relatively a less explored topic, enables several interesting applications. For instance, it allows authors to adjust their writing style to produce a more coherent document in collaboration. Similarly, style analysis can also be used for document provenance and authentication as a primary step. In this paper, we propose an ensemble-based text-processing framework for the classification of single and multi-authored documents, which is one of the key tasks in style analysis. The proposed framework incorporates several state-of-the-art text classification algorithms incl…
Authorship analysis (AA) is the study of unveiling the hidden properties of authors from a body of exponentially exploding textual data. It extracts an author's identity and sociolinguistic characteristics based on the reflected writing styles in the text. It is an essential process for various areas, such as cybercrime investigation, psycholinguistics, political socialization, etc. However, most of the previous techniques critically depend on the manual feature engineering process. Consequently, the choice of feature set has been shown to be scenario- or dataset-dependent. In this paper, to m…
Is AI Catching Up to Human Expression? Exploring Emotion, Personality, Authorship, and Linguistic Style in English and Arabic with Six Large Language Models
College of Computer and Information Sciences, King Saud University（计算机与信息科学学院，沙特阿拉伯国王沙德大学）
A Ship of Theseus: Curious Cases of Paraphrasing in LLM-Generated Texts
The Pennsylvania State University（宾夕法尼亚州立大学）；Kempelen Institute of Intelligent Technologies（肯佩伦智能技术研究所）；MIT Lincoln Laboratory（麻省理工学院林肯实验室）；Indiana University（印第安纳大学）
Overview of PAN 2026: Voight-Kampff Generative AI Detection, Text Watermarking, Multi-Author Writing Style Analysis, Generative Plagiarism Detection, and Reasoning Trajectory Detection
Friedrich Schiller University Jena（耶纳弗里德里希·施勒格尔大学）；University of Applied Sciences BFI（应用科学大学BFI）；Mohamed bin Zayed University of Artificial Intelligence（马尔代夫 bin Zayed 人工智能大学）；University of Kassel（卡塞尔大学）；INSAIT, Sofia University ``St. Kliment Ohridski''（INSAIT，索菲亚大学『圣克莱门特·奥赫里迪斯』）；University of Innsbruck（因斯布鲁克大学）
PAN 2026旨在通过客观评估推进计算风格学和文本取证，涵盖生成式AI检测、文本水印、多作者写作分析、抄袭检测及推理轨迹检测等五个新旧任务。
Despite the success of style transfer in image processing, it has seen limited progress in natural language generation. Part of the problem is that content is not as easily decoupled from style in the text domain. Curiously, in the field of stylometry, content does not figure prominently in practical methods of discriminating stylistic elements, such as authorship and genre. Rather, syntax and function words are the most salient features. Drawing on this work, we model style as a suite of low-level linguistic controls, such as frequency of pronouns, prepositions, and subordinate clause constru…
Style-Specific Neurons for Steering LLMs in Text Style Transfer
School of Computation, Information and Technology, Technical University of Munich（慕尼黑工业大学计算、信息与技术学院）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Center for Information and Language Processing, LMU Munich（慕尼黑大学信息与语言处理中心）
Authorship analysis is an important subject in the field of natural language processing. It allows the detection of the most likely writer of articles, news, books, or messages. This technique has multiple uses in tasks related to authorship attribution, detection of plagiarism, style analysis, sources of misinformation, etc. The focus of this paper is to explore the limitations and sensitiveness of established approaches to adversarial manipulations of inputs. To this end, and using those established techniques, we first developed an experimental frame-work for author detection and input pert…
Department of Jewish Literature, Bar-Ilan University（巴伊兰大学犹太文学系）；Department of Communication, Reichman University（雷赫曼大学传播系）；Faculty of Engineering, Bar-Ilan University（巴伊兰大学工程学院）
Sofia University “St. Kliment Ohridski”（索非亚大学“圣克莱门特·奥赫里德斯基”）；Qatar Computing Research Institute, HBKU（卡塔尔计算研究所，哈马德·本·哈利法大学）
Users posting online expect to remain anonymous unless they have logged in, which is often needed for them to be able to discuss freely on various topics. Preserving the anonymity of a text's writer can be also important in some other contexts, e.g., in the case of witness protection or anonymity programs. However, each person has his/her own style of writing, which can be analyzed using stylometry, and as a result, the true identity of the author of a piece of text can be revealed even if s/he has tried to hide it. Thus, it could be helpful to design automatic tools that can help a person obf…
Retrieving indexed documents, not by their topical content but their writing style opens the door for a number of applications in information retrieval (IR). One application is to retrieve textual content of a certain author X, where the queried IR system is provided beforehand with a set of reference texts of X. Authorship verification (AV), which is a research subject in the field of digital text forensics, is suitable for this purpose. The task of AV is to determine if two documents (i.e. an indexed and a reference document) have been written by the same author X. Even though AV represents …
