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
HyperStyler is a new architecture for low-resource authorship style transfer that separates style selection from style realization. It uses a style navigator to predict style coordinates from source context and target-author references, and a style hypernetwork to apply these coordinates through dynamic parameter modulation. Experiments on Reddit, Blog, and News datasets show that HyperStyler outperforms previous methods, including LLM-based approaches, while adding only 2.4% more parameters than T5-large and running 1.8× faster at inference.
By Jongkyung Shin, Minguk Jeon, Chanwoo Park, Chiehyeon Lim
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
The paper investigates how to adapt large language models to mimic an individual author's style using only a few example abstracts, a task made harder by the formal nature of scientific writing. It proposes three style‑conditioning methods—contrastive activation steering, a network predicting steering vectors, and a hypernetwork predicting LoRA adapters—and finds that while fine‑tuning captures the strongest style signal, it harms fluency; the hypernetwork offers the best balance between style imitation and output quality for both seen and unseen authors. The authors also show that steering can be performed at the author level by contrasting author abstracts against style‑neutral generations for the same content, eliminating the need for a predefined style inventory and outperforming inventory‑based approaches.
whyItMatters":"The study provides practical techniques for author‑style transfer in scientific writing, revealing a trade‑off between style fidelity and fluency and demonstrating that hypernetworks can effectively balance these aspects."
By Leonard Popp, Danni Liu, Supriti Sinhamahapatra, Jan Niehues
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 introduces a new authorship attribution model that represents each author with multiple consistent but distinct writing styles, rather than a single style profile. Experiments across four scenarios—audience size, temporal distance, preceding context, and genre norms—show that authors vary significantly in how idiolectic their writing is, and that certain settings yield text that is more informative about authorship. The authors demonstrate that modeling multiple styles per author leads to significant performance improvements over traditional single-style approaches.
By Omkar Yadav, Michael Jiang, Kenan Alkiek, Jian Zhu, David Jurgens