The paper introduces a training‑free method for uncovering prompt‑conditional stylistic axes in large language models (LLMs). By repeatedly sampling completions of a single prompt at high temperature and applying Principal Component Analysis (PCA) to the pooled hidden activations, the authors automatically label the resulting axes using the extreme (pole) generations. Validation against 245 human‑elicited stylistic annotations shows that, for the Qwen‑3.5‑4B‑Instruct model, the top two axes align with human dimensions with 72.8% precision and 43.6% macro‑recall, and 75.6% of validity ratings confirm the axes’ polar generations, while other models exhibit varying degrees of discoverability.
By Ajit Mallavarapu, Ziwei Gu
arXiv:2608.28986v1 Announce Type: new
Abstract: LLMs often struggle with modern Korean poetry, producing outputs that resemble "line-broken prose." We address two coupled tasks: detecting whether a K...
By Keunhyeung Park, Seunguk Yu, YoungBin Kim
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
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 investigates how post‑training of language models introduces or amplifies an AI‑like writing style and whether this style has a localized internal signature. By comparing human text, base‑model outputs, and aligned‑model outputs with matched human‑source prefixes, the authors find that aligned generations are less similar to human corpora and more easily detected as AI‑like. They propose PASTA, a training‑free method that estimates a post‑training alignment signature from residual contrasts and ablates the corresponding direction during decoding, reducing detection rates across multiple models and detectors while preserving relevance and coherence.
By Aniket Anand, Janvijay Singh, Zhewei Sun, Dilek Hakkani-T\"ur, Nick Feamster
arXiv:2609. 28245v1 Announce Type: cross Abstract: Large language models (LLMs) have shown strong performance in creative text generation, yet their ability to produce culturally grounded and stylistically constrained literary forms remains underexplored.
By AbdulRahman A. Morsy (Department of Computer Science, School of Engineering and Applied Sciences, George Washington University, Washington DC, United States), Aya Zirikly (Department of Computer Science, School of Engineering and Applied Sciences, George Washington University, Washington DC, United States, Center for Speech and Language Processing, Whiting School of Engineering, Johns Hopkins University, Baltimore MD, United States)
The study probes Gemma‑2‑9B‑IT with Sparse Autoencoders across English, Hebrew, and Russian to examine how multilingual LLMs handle informal register. By using a dataset of polysemous terms that appear in literal and informal contexts, the authors isolate pragmatic register processing from lexical cues. They discover a small, robust cross‑linguistic core that forms an informal register subspace, which becomes clearer in deeper layers and can causally shift output formality across all tested languages, even transferring zero‑shot to six unseen languages.
By Uri Z. Kialy, Avi Shtarkberg, Ayal Klein
arXiv:2607.25202v2 Announce Type: replace
Abstract: Conversational entrainment is well-studied in monolingual and written contexts, but remains underexplored in spoken code-switching (CSW). We presen...
By Debasmita Bhattacharya, Siying Ding, Alayna Nguyen, Julia Hirschberg
arXiv:2610.35008v1 Announce Type: new
Abstract: Chain-of-thought TTS systems rely on language-model planners to translate persona and context into vocal delivery, but it is unclear where this intent...
By Joonyong Park, Jerry Li
arXiv:2605.30526v2 Announce Type: replace
Abstract: Aligned language models often exhibit a recognizable AI-like style, yet its connection to post-training and internal representations remains poorly...
By Aniket Anand, Janvijay Singh, Zhewei Sun, Dilek Hakkani-T\"ur, Nick Feamster
arXiv:2610.02873v1 Announce Type: cross
Abstract: The evolution of linguistic style in conversations is an underexplored issue in NLP. Most style-control datasets focus on sentences or assume a stati...
By Vihindi Kotalawala, Pamoda Dilranga, Gayani Thoradeniya, Prasan Yapa
arXiv:2606. 31729v1 Announce Type: cross Abstract: Text-to-speech (TTS) evaluation is an open challenge.
By Dominika Woszczyk, Andreas Triantafyllopoulos, Jura Miniota, \'Eva Sz\'ekely, Bjoern Schuller