Authorship Attribution

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Theory-Grounded Evaluation Exposes the Authorship Gap in LLM Personalization
Apr 29, 2026Yash Ganpat SawantLLM Evaluation LLM Personalization
Independent AI Researcher
A Faceted Proposal for Transparent Attribution of AI-Assisted Text Production
Apr 28, 2026Geraldo XexéoAI Accountability Authorship Attribution
Programa de Engenharia de Sistemas e Computação, COPPE - Universidade Federal do Rio de Janeiro, Rio de Janeiro,RJ – Brasil[cs.CY]
Different Strokes for Different Folks: Writer Identification for Historical Arabic Manuscripts
Apr 24, 2026Hamza A. Abushahla, Ariel Justine N. Panopio, Layth Al-Khairulla +1Document Understanding Historical Document Analysis
Department of Computer Science and Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates
Rethinking Publication: A Certification Framework for AI-Enabled Research
Apr 23, 2026Yang Lu, Rabimba Karanjai, Lei Xu +1AI-Assisted Scientific Research Scholarly Peer Review
mcdok at SemEval-2026 Task 13: Finetuning LLMs for Detection of Machine-Generated Code
Apr 23, 2026Adam Skurla, Dominik Macko, Jakub SimkoAI-Generated Text Detection LLM Fine-Tuning
Faculty of Information Technology, Brno University of Technology, Brno, Czechia · Kempelen Institute of Intelligent Technologies, Bratislava, Slovakia
Explainable Disentangled Representation Learning for Generalizable Authorship Attribution in the Era of Generative AI
Apr 23, 2026Hieu Man, Van-Cuong Pham, Nghia Trung Ngo +2Variational Autoencoders Disentangled Representation Learning
University of Oregon, OR, USA · Adobe Research, CA, USA
Rank-Turbulence Delta and Interpretable Approaches to Stylometric Delta Metrics
Apr 21, 2026Dmitry Pronin, Evgeny KazartsevComputational Literary Studies Stylometry
HSE University
Assessing Capabilities of Large Language Models in Social Media Analytics: A Multi-task Quest
Apr 21, 2026Ramtin Davoudi, Kartik Thakkar, Nazanin Donyapour +2Social Media Analysis LLM Evaluation
Utah State University · Independent Researcher · Vanderbilt University
PeerPrism: Peer Evaluation Expertise vs Review-writing AI
Apr 16, 2026Soroush Sadeghian, Alireza Daqiq, Radin Cheraghi +3LLM Evaluation AI-Generated Text Detection
Reviewerly Toronto, Ontario, Canada · University of Toronto, Reviewerly Toronto, Ontario, Canada · UC Berkeley, Reviewerly Berkeley, California, United States
Hijacking Text Heritage: Hiding the Human Signature through Homoglyphic Substitution
Apr 11, 2026Robert DilworthStylometry Authorship Attribution
Department of Computer Science and Engineering, Mississippi State University, Mississippi State, Mississippi, USA
Who Wrote the Book? Detecting and Attributing LLM Ghostwriters
Mar 30, 2026Anudeex Shetty, Qiongkai Xu, Olga Ohrimenko +1OOD Generalization Language Model Fingerprinting
School of Computing and Information Systems, The University of Melbourne, Australia · School of Computing, FSE, Macquarie University, Australia
Interpretable Predictability-Based AI Text Detection: A Replication Study
Mar 16, 2026Adam Skurla, Dominik Macko, Jakub SimkoAI-Generated Text Detection Language Model Fingerprinting
Faculty of Information Technology, Brno University of Technology, Brno, Czechia · Kempelen Institute of Intelligent Technologies, Bratislava, Slovakia
One-shot Style Transfer LLM log-probabilities for Authorship Attribution and Verification
Oct 15, 2025Pablo Miralles-González, Javier Huertas-Tato, Alejandro Martín +1Stylometry Authorship Attribution
Department of Computer Systems Technical University of Madrid Madrid

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Theory-Grounded Evaluation Exposes the Authorship Gap in LLM Personalization

Yash Ganpat Sawant

Independent AI Researcher

LLM Evaluation LLM Personalization Language Model Generation Evaluation Authorship Attribution

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Stylistic personalization - making LLMs write in a specific individual's style, rather than merely adapting to task preferences - lacks evaluation grounded in authorship science. We show that grounding evaluation in authorship verification theory transforms what benchmarks can measure. Drawing on three measurement traditions - LUAR, a trained authorship verification model; an LLM-as-judge with decoupled trait matching; and classical function-word stylometrics - we evaluate four inference-time personalization methods across 50 authors and 1,000 generations. The theory-grounded metric, LUAR, provides what ad hoc alternatives cannot: calibrated baselines, with a human ceiling of 0.756 and a cross-author floor of 0.626, that give scores absolute meaning. All methods score below this floor, from 0.484 to 0.508, exposing an authorship gap invisible to uncalibrated metrics. The three metrics produce near-zero pairwise correlations, with absolute r less than 0.07, confirming that without theoretical grounding, metric choice determines conclusions: an LLM judge declares a clear winner while LUAR finds no meaningful differentiation. These findings demonstrate the theory-benchmark cycle in action: authorship theory exposes evaluation failures that ad hoc benchmarks miss.

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