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
arXiv:2609.22112v1 Announce Type: new
Abstract: Large language models (LLMs) have demonstrated the ability to generate user-specific text with high stylistic fidelity. However, the personal data that...
By Muhammed Nazmul Arefin, Omar Jamal Hammad
arXiv:2609.06771v1 Announce Type: cross
Abstract: Authorship signals matter in settings where writing style carries identity: digital forensics, plagiarism analysis, account linking, misinformation i...
By MaoXun Huang, Zhenxing Zhang, Claire Cardie
arXiv:2607. 29539v1 Announce Type: cross Abstract: Standard AI-text detection benchmarks compare human-written text against text generated directly by large language models (LLMs).
By Gaetano Perrone, Simon Pietro Romano
The study investigates whether large language models (LLMs) can identify code they have generated, potentially leading to self‑favoring or collusive behavior. Experiments across 15 model‑benchmark pairs show that models can attribute authorship with balanced accuracy between 49% and 58%, but this ability largely stems from superficial cues such as solution length. Removing surface features like docstrings, comments, and type hints reduces attribution accuracy to chance, indicating that surface cues drive the effect.
By Ehsan Barkhordar, Surendrabikram Thapa
The paper introduces FAVoR, a method for federated personalized generation that mitigates author‑style homogenization caused by standard aggregation in parameter‑efficient fine‑tuning. Using the BlogText benchmark and ASCE diagnostics, the authors show that common federated PEFT baselines preserve semantic utility but blur author‑specific style. FAVoR employs a shared‑private adapter design, where clients upload shared updates while keeping author‑specific residual corrections locally, leading to improved style retention with minimal utility loss.
By Lu Han, Jingyao Zhang, Katy Ilonka Gero, Nguyen H. Tran