arXiv:2310.00436v2 Announce Type: replace
Abstract: Authorship identification uses patterns in writing to infer who wrote a text, but those patterns also reflect topic, genre, and register. This surv...
By Haining Wang
The paper "Evaluating Style-Personalized Text Generation: Challenges and Directions" examines the difficulties of assessing text that is tailored to individual users’ styles. It critiques common metrics such as BLEU, embeddings, and LLM-as-judges, and introduces a style discrimination benchmark covering domain discrimination, authorship attribution, and LLM-generated personalized versus non-personalized discrimination across eight writing tasks. The study finds that ensembles of diverse evaluation metrics outperform single-evaluator approaches and offers guidance for reliable assessment of style-personalized generation.
By Anubhav Jangra, Bahareh Sarrafzadeh, Silviu Cucerzan, Adrian de Wynter, Sujay Kumar Jauhar
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 dis...
The paper examines how different author representations affect zero‑shot authorship attribution (AA). It compares a label‑only prompting baseline with three strategies: representative writing samples, LLM‑generated descriptions, and style embeddings (LISA). The study finds that incorporating author‑specific representations improves performance, with a two‑stage LISA framework achieving the best results, while LLM‑generated descriptions offer a more compact but slightly less accurate alternative.
By Nudrat Habib, Tosin Adewumi, Sana Sabah Al-Azzawi, Marcus Liwicki, Elisa Barney
The paper investigates whether activation steering can capture the complex, multidimensional nature of authorship style. By using structured contrastive prompting along rhetorically motivated dimensions, the authors construct rich style representations directly in activation space, revealing a shared authorship backbone with aspect‑specific residuals. They propose Aspect‑Aware Activation Steering (A3S), a training‑free framework that merges per‑aspect contrastive directions, employs interference‑aware aggregation, and tunes steering strength per instance, achieving better authorship style transfer and outperforming a trained baseline on out‑of‑domain benchmarks.
By Hieu Tran, Calvin Bao, Marine Carpuat
arXiv:2510.13302v4 Announce Type: replace-cross
Abstract: Computational stylometry studies writing style through quantitative textual patterns, enabling applications such as authorship attribution, i...
By Pablo Miralles-Gonz\'alez, Javier Huertas-Tato, Alejandro Mart\'in, David Camacho