AuthorMix is a lightweight, modular framework for authorship style transfer that uses layer‑wise adapter mixing. It trains individual style‑specific LoRA adapters on a small set of high‑resource authors, enabling rapid adaptation to new target styles with only a few examples. The method achieves the highest combined style‑meaning score among baselines, including GPT‑5.1, and improves meaning preservation, as confirmed by human evaluation.
By Sarubi Thillainathan, Ji-Ung Lee, Michael Sullivan, Alexander Koller
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
arXiv:2606. 31741v1 Announce Type: cross Abstract: While semantic embeddings are rigorously evaluated on the Massive Text Embedding Benchmark, the evaluation of style embeddings remains fragmented, with each work relying on their own set of tasks and datasets.
By Rafael Rivera Soto, Anna Wegmann, Cristina Aggazzotti
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 introduces GLASS, a training‑free framework that personalizes text generation by combining global user‑style priors extracted with sparse autoencoders and local contrastive style vectors built from clustered interaction scenarios. During inference, GLASS injects these global and local vectors into different model layers, achieving context‑aware personalization without retrieval or parameter updates. Experiments on LaMP and LongLaMP datasets demonstrate that GLASS surpasses retrieval, fine‑tuning, and other steering baselines on ROUGE metrics and LLM‑as‑judge evaluations, and its SAE‑based representations show robustness to topic and length shifts, indicating better disentanglement of style from content.
By Liuji Chen, Zeyu Zhang, Xinyuan Zhang, Shuai Nie, Qiang Liu, Shu Wu, Liang Wang
arXiv:2602.08059v2 Announce Type: replace-cross
Abstract: Text-to-image diffusion models can reproduce specific artists visual styles at extremely low cost, raising copyright and deployment safety co...
By Tong Zhang, Ru Zhang, Jianyi Liu